SARI-LEENA HIMANEN
A New Visual Adaptive Scoring System
for Sleep Recordings
Development and Application to the
Multiple Sleep Latency Test
U n i v e r s i t y o f Ta m p e r e
Ta m p e r e 2 0 0 0
A New Visual Adaptive Scoring System
for Sleep Recordings
A c t a U n i v e r s i t a t i s T a m p e r e n s i s 7 69
AC ADEMIC
DISSERTAT I O N
University of Tampere, Medical School
Tampere University Hospital,
Department of Clinical Neurophysiology
Finland
Supervised by
Reviewed by
Docent Joel Hasan
University of Tampere
Docent Ilkka Lehtinen
University of Turku
Docent Uolevi Tolonen
University of Oulu
Distribution
University of Tampere
Sales Office
P.O. Box 617
33101 Tampere
Finland
Tel. +358 3 215 6055
Fax +358 3 215 7150
[email protected]
http://granum.uta.fi
Cover design by
Juha Siro
Printed dissertation
Acta Universitatis Tamperensis 769
ISBN 951-44-4925-8
ISSN 1455-1616
Tampereen yliopistopaino Oy Juvenes Print
Tampere 2000
Electronic dissertation
Acta Electronica Universitatis Tamperensis 61
ISBN 951-44-4926-6
ISSN 1456-954X
http://acta.uta.fi
SARI-LEENA HIMANEN
A New Visual Adaptive Scoring System
for Sleep Recordings
Development and Application to the
Multiple Sleep Latency Test
ACADEMIC DISSERTATION
To be presented, with the permission of
the Faculty of Medicine of the University of Tampere,
for public discussion in the small auditorium of Building B,
Medical School of the University of Tampere,
Medisiinarinkatu 3, Tampere, on October 27th, 2000, at 12 o’clock.
U n i v e r s i t y o f Ta m p e r e
Ta m p e r e 2 0 0 0
CONTENTS
ABBREVIATIONS
9
1. INTRODUCTION
11
2. REVIEW OF THE LITERATURE
12
2.1. Evolution of sleep EEG description
2.1.1. Early days of sleep staging
2.1.2. Further development of the sleep process
12
12
13
2.2. Neurophysiological basis of NREM sleep and NREM sleep EEG
2.2.1. Slow oscillation
2.2.2. Impact of slow oscillation on cortical EEG
2.2.3. Other periodicities in sleep
2.2.4. Cyclic Alternating Pattern
13
13
14
15
16
2.3. Standardised scoring system
2.3.1. Rules of the standardised scoring system
2.3.2. Evaluation of the standardised sleep stage scoring system
Scoring with epochs ignores short lasting oscillations
Topographical aspects
RKS does not take into account the continuity of the sleep
process and phasic events
RKS is not sufficient to describe drowsy states
Atypical PSG patterns
Summary of the criticism of RKS
2.3.3. Visual methods to supplement and improve RKS
18
18
19
19
19
2.4. Computerized sleep analysis
2.4.1. Sleep stage scoring
2.4.2. Spectral analysis
2.4.3. FFT in sleep-wake transition
Topographical studies by FFT
25
25
26
27
28
2.5. Adaptive segmentation
2.5.1. Automatic adaptive segmentation
2.5.2. Visual adaptive segmentation
29
29
31
2.6. Multiple sleep latency test in vigilance study
2.6.1. Definition of MSLT
2.6.2. Problem of sleep onset
Moment of sleep onset by EEG, subjective assessment,
reaction times and hypnagogic imageries
SEMs at sleep onset
Summarising the definition of sleep onset
31
31
32
2.7. Value of MSLT in determining sleepiness
2.7.1. Positive findings
2.7.2. Negative findings
2.7.3. MSLT and nocturnal polygraphic parameters
34
34
34
35
5
20
21
23
23
24
32
33
33
2.7.4. MSLT in sleep apnea syndrome
2.7.5. Evaluation of MSLT as an indicator of sleepiness
2.7.6. Increasing the sensitivity of MSLTs
36
37
38
3. PURPOSE OF THE STUDY
40
4. SUBJECTS AND METHODS
41
4.1. Subjects
41
4.2. Recordings
42
4.3. Psychometric tests
42
4.4. Visual scoring
4.4.1. Night recordings
4.4.2. MSLT scoring
Repeatability of VASS
Definition of MSLT latencies
43
43
45
47
48
4.5. Spectral analysis
4.5.1. Calculation of EEG spectra
4.5.2. Comparison between RKS and VASS in the differentiation of
wakefulness and light sleep by alpha and delta-theta bands
4.5.3. Peak frequencies
4.5.4. Frequency band power characteristics of VASS stages
48
48
49
49
49
4.6. Statistical methods
50
4.7. Ethical considerations
50
5. RESULTS
51
5.1. Sleep parameters of night recordings
51
5.2. Sleep in MSLT naps: comparison between
patient and control groups
5.2.1. General characteristics
5.2.2. Sleep parameters in MSLT recordings
5.2.3. MSLT latencies
52
52
52
53
5.3. Characteristics of VASS parameters with comparison to RKS
5.3.1. MSLT parameters
5.3.2. Latencies
56
56
58
5.4. Clinical evaluation of MSLT
62
5.5. Effects of VASS on sleep analysis
5.5.1. Effects of VASS on epoch lengths
5.5.2. Stage transitions in VASS
5.5.3. Effects of VASS on hypnograms
63
63
65
67
6
5.6. RKS / VASS agreement
69
5.7. Periodicities at sleep onset
71
5.8. Psychometric tests
5.8.1. Comparison between patient and control groups
5.8.2. Correlation coefficient analysis
5.8.3. Stepwise binary logistic regression analysis
73
73
74
76
5.9. Repeatability of VASS
76
5.10. Spectral database
78
5.11. Spectral comparison between RKS and VASS
5.11.1. Differentiation between wakefulness and S1 by
alpha and delta-theta power bands
5.11.2. Topographical differences in alpha and delta-theta power bands
5.11.3. Peak frequencies of RKS and VASS stages
5.11.4. Summary of differences between RKS and VASS
79
79
81
85
87
5.12. Frequency band power characteristics of VASS stages
5.12.1. Peak frequency topography in VASS
5.12.2. Topographical band power differences within stages
5.12.3. Band power differences between stages
87
87
91
97
5.13. Band power differences between adjacent VASS stages
5.13.1. WA versus SA
5.13.2. WAF versus SAF
5.13.3. WL versus DL
5.13.4. S1VASS versus S2VASS
5.13.5. Aa2 versus S2VASS
99
99
100
100
100
100
6. DISCUSSION
101
6.1. Temporal resolution
6.1.1. Hypnograms
6.1.2. Quantitative parameters reflecting increased temporal resolution
101
101
102
6.2. MSLT and nocturnal parameters, psychometric
tests and subjective assessment
6.2.1. Nocturnal parameters
6.2.2. Effects of VASS in clinical evaluation of the MSLT
6.2.3. Psychometric tests
6.3. Benefits of VASS on the study of sleep dynamics
103
103
104
104
106
6.4. Spectral comparison between RKS and VASS
6.4.1. Alpha and delta-theta power
6.4.2. Differences in peak frequencies between RKS and VASS
6.4.3. VASS as a basis for automatic analysis
108
108
109
109
7
6.5. Frequency band power characteristics of VASS stages
6.5.1. Topographical differences of the peak frequencies in VASS
6.5.2. Band power differences between adjacent VASS stages
WA versus SA
WAF versus SAF
WL versus DL
S1VASS versus S2VASS
Aa2 versus S2VASS and VASS alpha stages
EMGW and MTW
6.5.3. Band power characteristics along with VASS stages
Alpha power
Theta power
Delta power
Sigma power
Beta power
110
110
110
110
111
111
112
112
112
112
112
113
114
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115
6.6. VALIDITY OF STAGE DIVISION BY MORPHOLOGY
116
6.7. FFT AS QUALITY CONTROL OF VISUAL SCORING
118
6.8. VASS AS A SCORING SYSTEM
6.8.1. Repeatability of VASS
6.8.2. VASS in clinical practice
6.8.3. Disadvantages of VASS
6.8.4. Advantages of VASS
6.8.5. Position of VASS in sleep studies
118
118
118
119
119
120
7. CONCLUSIONS
122
8. SUMMARY
123
ACKNOWLEDGEMENTS
125
REFERENCES
127
APPENDIX
140
8
ABBREVIATIONS
Aa2
AD-test
AHI
ASDA
ASDARI
AwakeI
Bf-S
CAP
Cz
C4/A1
C3/A2
C4-M1
C3-M2
DL
EDS
EEG
EMG
EMGS
EMGW
EOG
EOG P8-M1
EOG P18-M1
ESS
FFT
FM-test
Fpz
Fz
Fp1-M2
Fp2-M1
Hz
ICSD
LatCon5-15
LatCum5-60
LatDL
LatHYP
LatSEM
LatS1RKS
LatS1VASS
LatS2RKS
LatS2VASS
LatVASS
mARI
Max
mESS
Min
MMSE
MSLT
MT
MTS
Alpha arousal from S2
Alphabetical Cancellation Test
Apnea-hypopnea index
American Sleep Disorders Association
Arousal index according to ASDA
> 30 s awakenings / h of sleep
Well-being Scale
Cyclic Alternating Pattern
Electrode position at the vertex
Electrode derivation right central referred to left earlobe
Electrode derivation left central referred to right earlobe
Electrode derivation right central referred to left mastoid
Electrode derivation left central referred to right mastoid
Drowsy-low
Excessive daytime sleepiness
Electroencephalography
Electromyography, muscle tonus
EMG activity in sleep
EMG activity in wakefulness
Electro-oculography
EOG derivation above right eye referred to left mastoid
EOG derivation below left eye referred to left mastoid
Epworth Sleepiness Scale
Fast Fourier Transformation
Gruenberger Fine-motor Test
Midline fronto-polar EEG electrode position
Midline frontal EEG electrode position
Electrode derivation left fronto-polar referred to right mastoid
Electrode derivation right fronto-polar referred to left mastoid
Cycles per second
International Classification of Sleep Disorders
Mean latency to the beginning of 5-15 s of continuous sleep
Mean latency to 5-60 s of cumulative sleep
Mean latency to DL
Mean latency to first hypopnea/apnea if present before S1RKS
Mean latency to first appearance of SEMs
Mean latency to S1RKS
Mean latency to S1VASS
Mean latency to S2RKS
Mean latency to S2VASS
Shorter of LatDL/LatS1VASS
Modified arousal index
Maximum
Modified Epworth Sleepiness Scale
Minimum
Mini Mental State Examination
Multiple sleep latency test
Movement time
Movements in sleep
9
MTW
M2-M1
NREM sleep
ODI 4
OSAS
Oz
O1-M2
O2-M1
PLMS
PSG
PSQI
Pz
P4-O2
QOL
REM
RKS
RR interval
RT-miss
RT-test
s
SA
SAF
SaO2min
SAS
SDS
SEI
SEM
ShInd
ShiRKS
ShiVASS
SOL
SPT
SREM
SWS
S0
S1
S2
S3
S4
S0RKS
S1RKS
S2RKS
S1VASS
S2VASS
TIB
TST
WA
WAF
VASS
VIGdiff
Vigil
WL
Movements in wakefulness
Electrode derivation between mastoids
Non-REM sleep
Oxygen desaturation index (> 4 %)
Obstructive sleep apnea syndrome
Midline occipital EEG electrode position
EEG derivation left occipital referred to right mastoid
EEG derivation right occipital referred to left mastoid
Periodic limb movement syndrome
Polygraphy
Pittsburgh Sleep Quality Index
Central parietal EEG electrode position
EEG derivation right parietal referred to right occipital
Quality of Life Index
Rapid eye movement
Rechtschaffen and Kales scoring system
Pulse interval
Misses in reaction time test
Mean response time in reaction time test
Second
Alpha-SEM, occipital alpha activity with SEMs
Alpha-SEM-F, diffuse or fronto-central alpha with SEMs
Minimum oxygen saturation
Zung Anxiety Scale
Zung Depression Scale
Sleep efficiency index
Slow eye movement
Stage shift index
Stage shift index in RKS
Stage shift index in VASS
Sleep onset latency
Sleep period time
REM sleep, stage REM
Slow wave sleep, stages 3 + 4
Stage 0, wakefulness
Stage 1
Stage 2
Stage 3
Stage 4
S0 in RKS
S1 in RKS
S2 in RKS
S1 in VASS
S2 in VASS
Time in bed
Total sleep time
Wake-alpha, occipital alpha activity without SEMs
Wake-alpha-F, diffuse or fronto-central alpha without SEMs
Visual Adaptive Scoring System
Difference between third and first part of vigilance test
Mean response time in vigilance test
Wake-low
10
1. INTRODUCTION
The standardised scoring system developed by the Committee led by Allan Rechtschaffen
and Anthony Kales was introduced to reduce variability in sleep recordings and analysis.
This includes the standardisation of recording techniques, stage definitions and
terminology. The scoring system of Rechtschaffen and Kales (RKS, 1968) provided
increased reliability and consistency in sleep analysis and enabled the use of quantitative
sleep parameters. The system is still widely used as a gold standard both in research and
clinical practice.
According to RKS sleep is divided into four non-REM stages and stage REM. The method
was designed for paper recordings leading to the use of fixed epochs lasting 20 or 30 s.
This type of classification with discrete stage definitions is especially problematic in the
multiple sleep latency test (MSLT). Sleep onset does not take place abruptly; vigilance
fluctuates between wakefulness and sleep as well as subvigilant stages before the subject
falls asleep (Roth 1961, Ogilvie and Wilkinson 1984, Badia et al. 1994). The long epoch
does not allow a description of this dynamics of sleep onset. The stage categories are also
too few for the presentation of the various drowsiness states.
MSLT is widely used to expose excessive daytime sleepiness. The associations between
MSLT defined sleepiness and other objective measures of sleepiness have been variable
(Johnson et al. 1990, Harrison and Horne 1996a). Similar results have been obtained when
MSLT scores have been correlated with psychometric parameters and subjective
assessments reflecting sleepiness (Pressman and Fry 1989, Chervin et al. 1995). Low
MSLT scores indicating increased sleepiness have been found in subjects without
vigilance complaints (Roth et al. 1980, Manni et al. 1991, Harrison and Horne 1996a,
Geisler et al. 1998).
Hori has divided wakefulness and light sleep into 9 stages (Hori et al. 1991, Hori et al.
1994). This division enabled an improved description of the sleep onset period from alert
wakefulness to sleep stage 2, which is considered “true sleep“ but Hori’s staging is based
on the use of fixed epochs. Temporal resolution can be improved by adaptive
segmentation (Praetorius et al. 1977). The methods have until now been computerized and
the applications in sleep research limited.
New methods for analysing the sleep process and sleep onset are required. These should be
based on more detailed stage definition and adaptive segmentation. This is also important
for increasing the validity of automatic sleep analysis. The new approach might also
improve the sensitivity and specificity of MSLT.
11
2. REVIEW OF THE LITERATURE
2.1. EVOLUTION OF SLEEP EEG DESCRIPTION
2.1.1. Early days of sleep staging
Loomis and co-workers were the first to describe different states of sleep based on the
electroencephalogram (EEG, Loomis et al. 1937). They presented five sleep states
characterised by differences in EEG potentials: A - alpha state, alpha rhythm in trains with
or without slow rolling eye movements. B - low voltage state, no alpha rhythm, a fairly
straight record with or without rolling slow eye movements (SEMs). C - spindle state, 14
Hz sleep spindles every few seconds. D - spindles plus random state, spindles with slow
large random potentials up to 300 µV high. E - random state, the large random potentials
coming from all parts of the cortex, spindles become inconspicuous. The stages were
suitable for high-alpha producers, with non-alpha type individuals it was more difficult to
distinguish between states A and B. States were noticed to shift continuously upward and
downward. State E was most solid for different kinds of stimuli. Changes in the states
could, however, occur even without any external stimulus. Two EEG derivations were
used, one central and one occipital. Some topographical differences were noticed between
EEG electrodes. Alpha activity seemed to be usually of greatest amplitude occipitally, but
could be highest frontally. Spindles were most marked on the top of the head and random
patterns came from all parts of the head. The authors also paid attention to the changes in
alpha activity even during wakefulness. Moreover, they observed that in the dark, alpha
activity could persist even if the eyes were opened. Dreaming was noticed to occur in state
B.
In 1953 a new type of eye movement in sleep, rapid eye movements (REMs), was
described by Aserinsky and Kleitman (1953). They found 3-4 periods with REMs in a
night sleep and paid attention to the cyclicity with which these periods occurred. They
were able to combine the periods with REMs to dreaming and also to the changes in the
activity of the autonomic nervous system.
Shortly afterwards Dement and Kleitman (1957) introduced the cyclic patterns of sleep
stages with cyclic periods of REMs as well. They divided sleep into four stages with stage
1 corresponding to the state B and partly to the state A of Loomis. Stage 2 corresponded to
state C, stage 3 to state D and stage 4 to state E. Amplitude and frequency criteria for delta
waves were implemented. Scoring could be performed as an on-line procedure. According
to their results sleep stage 1 could be divided into two entities; first, stage 1 is seen at sleep
onset, and second, cyclically through the night combined with REMs. Dreaming was seen
in stage 1 with REMs whereas in stage 1 at sleep onset hypnagogic reveries might occur.
The cyclicity of stages was visualised by hypnograms. Sleep stages were noticed to form
cycles where some stages often followed each other while some stage changes occurred
very seldom (Williams et al. 1964, Williams and Williams 1966). The slowest and highest
waves were usually seen in the first cycle. Stage 3 and especially stage 4 were only
occasionally seen during the second half of the night (Dement and Kleitman 1957). These
12
four non-REM (NREM) stages and stage REM (SREM) formed the basis of subsequent
polygraphic sleep studies.
2.1.2. Further development of the sleep process
Feinberg (1974) was one of the first to provide a detailed description of sleep cycle
changes with age. Sleep stage 4 dominates the early cycles, especially in younger age
groups. In older age groups the amount of stage 4 is diminished, but stage 3 seems to
increase. Feinberg’s conclusion was that stage 3 represents the same process as stage 4, but
in a weaker version. Age did not have an effect on SREM period duration. As the decline
of slow wave activity over consecutive sleep cycles was well documented and slow wave
sleep seemed to have an increasing trend after sleep deprivation (Webb and Agnew 1971)
Feinberg presented a theory that the purpose of stage 4 was to reverse the metabolic or
neuronal effects caused by wakefulness (Feinberg 1974, Feinberg et al. 1984). Thus the
longer wakefulness before going to sleep, the more intense slow wave sleep. This was also
supported by the finding that late daytime napping reduces stage 4 the following night
(Karacan et al. 1970). The alternating pattern between REM and NREM sleep was
explained by the higher priority of stage 4 sleep. With sleep the demand for stage 4 is
reduced and the demand for SREM exceeds it. That is when SREM can begin. Later
SREM gives way to the greater demand for slow wave sleep.
The two-process model of sleep regulation presented by Borbély a few years later is based
on the assumption that instead of one, there are two separate processes underlying sleep
regulation (Borbély 1982, Borbély 1984). One is a sleep-dependent homeostatic process,
“Process S“, which rises exponentially during waking and shows an exponential decline
during sleep. It corresponds to the declining trends of both slow wave sleep and the slow
wave activity of sleep EEG (Borbély et al. 1981, Borbély 1982, Brunet et al. 1988). The
other process is the circadian process, “Process C“ which is independent of prior sleep and
waking. The determining role of time of day in sleep regulation was demonstrated by
Åkerstedt and Gillberg (1981). The phase position of Process C was based on the work of
Åkerstedt and Fröberg (1977). Sleep propensity is determined by the difference between
the two processes. As NREM sleep propensity is a reflection of the homeostatic process,
REM sleep propensity can be considered a reflection of the circadian process (Borbély
1984).
2.2. NEUROPHYSIOLOGICAL BASIS OF NREM SLEEP AND NREM SLEEP EEG
2.2.1. Slow oscillation
Recent experimental animal studies have shown that the principal process behind NREM
sleep is slow oscillation of < 1 Hz (Steriade et al. 1993a, Achermann and Borbély 1997,
Amzica and Steriade 1998a, Steriade and Amzica 1998a). The frequency of slow
oscillation varies between 0.5-0.9 Hz, the depolarising phase lasting for 0.4-0.8 s while the
hyperpolarization lasts for 0.3-0.7 s (Steriade and Amzica 1998b). The slow oscillation
seems to be generated in the cortex, since it is present in athalamic cats (Steriade et al.
13
1993b) but in decorticated cats it cannot be seen (Timofeev and Steriade 1996). The
depolarising component is generated out of excitatory and inhibitory synaptic potentials
(Steriade and Amzica 1998a). The hyperpolarization is associated with decreasing firing of
the cortical network (Steriade and Amzica 1998a), being associated with decreased
facilitation of the network (Contreras et al. 1996). The slow after-hyperpolarization is
abolished by acetylcholine in cortical slices in vitro (McCormick and Prince 1986). In vivo
sensory input via the brainstem systems can interfere with slow oscillation (Steriade et al.
1993d). Slow oscillation is synchronised widely over the cortical surface by intracortical
linkages (Amzica and Steriade 1995). It is also described in thalamocortical and thalamic
reticular neurons (Steriade et al. 1993c) and at least some other brain sites.
2.2.2. Impact of slow oscillation on cortical EEG
The appearance of various EEG rhythms and patterns in NREM sleep can be explained by
slow oscillation. The hyperpolarizing-depolarising sequence on the cellular level gives rise
to a K complex (KC). Several KCs together at the EEG level display the rhythmicity of the
slow oscillation (Amzica and Steriade 1997).
At sleep onset the constant excitatory input via the midbrain reticular formation and
mesopontine cholinergic nuclei to the thalamus and cortex is reduced, allowing the
hyperpolarization of thalamocortical cells to occur (see Amzica and Steriade 1998a). This
leads to the increasing sensory deafferentation of the cortex. In this state of vigilance the
EEG shows decreased amplitude, and, according to Amzica and Steriade (1998a), no sleep
oscillations are present. At this time the synchronisation and the level of the slow
oscillation of the cortical network is low. Some vertex waves can be seen in the EEG.
With further synchronisation and spreading of the slow oscillation over the cortical surface
the vertex waves become more visible and their amplitude increases until they assume the
form of the KCs (Amzica and Steriade 1998b). The incidence of KCs expresses the
increasing synchronisation and with deepening of sleep and sensory deafferentation KCs
become more rhythmic (Amzica and Steriade 1997). Thus spontaneous KCs can be
regarded as an oscillatory phenomenon in contrast to the evoked KCs (Amzica and
Steriade 1998b).
Widening slow oscillation spreads to the thalamus in synchronous volleys. In the thalamus
it starts to drive spindles produced by thalamic reticular and thalamocortical cells
(Morison and Basset 1945) to the cortex through the thalamocortical network (Steriade et
al. 1990, Contreras and Steriade 1996, see Amzica and Steriade 1998b). Thus during light
sleep every cycle of slow oscillation usually leads to a spindle sequence (Steriade and
Amzica 1998a). The spindles are considered to be sleep maintaining events (Naitoh et al.
1982, Jankel and Niedermeyer 1985) which block the transfer of sensory information into
the cortex allowing the evolution of sleep into deeper stages (see discussion after Steriade
1992, Timofeev et al. 1996). In this sleep phase the cortical EEG shows KCs and spindles
(Amzica and Steriade 1998a).
14
Thalamocortical neurons can also generate clock-like oscillations with the frequency of 14 Hz (McCormic and Pape 1990, Leresche et al. 1991, Steriade et al. 1991). With
increasing hyperpolarization of the thalamocortical cells spindles are gradually replaced by
this intrinsically generated delta activity (Steriade et al. 1991). The slow oscillation also
synchronises this activity to spread all over the cortex (Amzica and Steriade 1997, Amzica
and Steriade 1998b). This state corresponds to the slow wave sleep of humans (stage 3). In
stage 4 of human sleep the hyperpolarization level is highest (Amzica and Steriade 1998a).
Cortical neurones can also generate delta activity. This delta activity may also be
synchronised by the slow oscillation. The different patterns together result in the complex
waveforms seen in the EEG during NREM sleep (see Steriade and Amzica 1998a). That
slow oscillation and delta activity are also two distinct entities in humans has been
demonstrated by Achermann and Borbély (1997).
Steriade has described a double frequency for spindles in cats. Spindles consist of the
activity between 7-14 Hz lasting for 1-2 s repeating periodically at a frequency of 0.1-0.3
Hz (Steriade 1993, Steriade 1994). In humans spindles follow one another at intervals of
about 4 s (Kubicki et al. 1986, Spieweg et al. 1992, Evans and Richardson 1995,
Achermann and Borbély 1997). This 4 s periodicity (0.25 Hz) is somewhat slower than the
frequency band of slow oscillation. Achermann and Borbély (1997) point out that if this
same slow mechanism explains the 4 s periodicity, the phase of hyperpolarization must be
longer in humans than in cats. Whether or not the periodicity of the spindles is an
expression of slow oscillation in humans is still obscure.
The two-process model of Borbély and coworkers (see above) implies that there is a
homeostatic and a circadian component, which regulate the sleep/wake and NREM/REM
interactions. The question remains whether hyperpolarization with slow oscillation is the
true representative of the homeostatic component.
2.2.3. Other periodicities in sleep
Besides the slow oscillation several other periodicities exist in sleep. The most prominent
is the above-mentioned 90-100 min. sleep cycle, which was originally described by
Dement and Kleitman (1957). It has been proposed that the NREM-REM sleep cycle is
generated by the reciprocal interaction of REM-on and REM-off neurons in the brainstem
(McCarley 1994).
Shorter periodicities can also be found in normal sleep. Lugaresi at al. (1972) described
several vegetative and somatic phenomena (systemic arterial pressure, pulmonary arterial
pressure, cardiac rate, arteriolar tone, breathing, peripheral motoneurone excitability, level
of consciousness) which tend to oscillate or repeat themselves periodically every 20 - 30 s
especially during light sleep.
Roth (1961) paid attention to the EEG fluctuations during sleep onset. The durations of the
stages with stationary EEG activity varied between 2 - 20 s. The periods were shorter at
the beginning of the vigilance fluctuation, lengthening with time. Continuous short state
changes during lowered vigilance and at sleep onset were also noticed by Oswald (1962).
15
The fluctuating nature of sleep onset can be visualised by scoring with short 5 s epochs
(Badia et al. 1994). It has been postulated that the beginning of sleep in fact consists of
multiple sleep onsets (Ogilvie and Wilkinson 1984). On the other hand sleep onset can be
considered to consist of brief microsleeps which usually appear as close clusters
(Guilleminault et al. 1975).
Evans also studied the naturally occurring fluctuation of EEG measured vigilance. She
observed that the EEG episodes at sleep onset consisted of periods of higher arousal
associated with a shift towards wakefulness and periods of lower arousal with a shift
towards sleep (Evans 1992, Evans 1993). In stage 1 the dominant interval of the
alternating alpha and theta periods was 16 s with other possible peaks of about 10-12 s and
24-26 s. In deeper NREM sleep (stage 2, stage 3 and stage 4) the sleep stages alternated
with arousals. “Definite Arousals“ in stage 2, stage 3 and stage 4 were defined by the
presence of 4 of the following patterns: alphaburst, one or more KCs, SEMs, an increase in
EMG activity, a change in respiration and/or beat-to-beat heart rate. The definition used
for arousals was more liberal than is suggested by the atlas task force of the American
Sleep Disorders Association (ASDA 1992). In the ASDA criteria arousal is defined by a
shift in EEG frequency to theta, alpha and/or faster frequencies, but not spindles. In
Evans’ study the dominant interval between higher and lower arousal states in stage 2 was
51 - 60 s. Definite arousal activity was sparser in stages 3 and 4. The intervals between
definite arousals lengthened as sleep became sounder. On the grounds of this work sleep
stage 1 could be seen as an alternating pattern of alpha and theta activity instead of a
discrete stage.
Three dominant slow EEG periodicities of theta, alpha and beta power bands have also
been observed during wakefulness (Novak et al. 1997). The fastest, with a periodicity of
10 s was related to the respiratory rate. The two other dominant rhythms had periods of 18
– 20 s and about 46 s. The two fastest periodicities are close to those observed in RR
intervals. Thus it seems that EEG periodicities are related to the functions of the
autonomic nervous system. All three periodicities correspond to those found by Evans
(1992, 1993) during sleep onset. The slowest periodicity is close to the cyclic alternating
pattern (CAP).
Schieber and co-workers have described the alternation between sleep and short arousals
as “les phases d’activation transitoire“, which are regarded as being part of the normal
sleep process (see: Muzet et al. 1991, Paiva and Rosa 1994). Frequent short micro-arousals
in light sleep decreasing in deeper sleep have been observed by Halász et al. (1979).
2.2.4. Cyclic Alternating Pattern
Terzano and co-workers have introduced the cyclic alternating pattern (CAP, Terzano et
al. 1985). CAP is periodic EEG activity consisting of two different alternating EEG
patterns. It can be divided into two phases, phase A and phase B, each lasting more than 2
s and generally less than 60 s. This cyclicity is related to fluctuation of vigilance between
two sleep levels. The two phases represent different morphology and different
responsiveness to stimulation. By stimulus experiments phase A has been proven to
represent the higher arousal level. CAP phases tend to recur at 20 to 40 s intervals. One
16
CAP cycle consists of phase A followed by phase B (Terzano et al. 1985, Terzano and
Parrino 1991). Originally cyclic alternation of EEG patterns was described in comatose
patients and in Creutzfeldt-Jakob disease (Evans 1975, Terzano et al. 1981).
CAP phases have different morphology depending on the sleep stage in which they occur.
Microarousals in all stages are taken into account as phase A. In sleep stage 1 phase A
consists of alpha activity or vertex wave sequences and phase B corresponds to the
disappearance of the alpha activity. In stage 2 phase A consists of KC sequences or KCs
with alpha-activity. In stages 3 and 4 phase A consists of delta bursts (Parrino et al. 1996).
No equivalent rhythmic pattern has been found in normal REM sleep, but with severe
sleep fragmentation, as in obstructive sleep apnea syndrome, CAP sequences may also be
present in REM sleep (Parrino et al. 2000).
Functionally CAP phase A events correspond to transient arousals (Parrino and Terzano
1996). CAP extends the arousal definition formulated by ASDA (1992). Many of the CAP
phase A patterns are not included in the ASDA arousal criteria. The minimum duration
request of an ASDA arousal is 3 s while that of CAP phases is 2 s.
CAP rate is defined as a percentage ratio of total CAP time in NREM sleep to total NREM
sleep time. In young adults CAP rate is normally about 23%. It is age-related, expressing a
minimum in young humans, increasing with age (Terzano and Parrino 1993). With high
sleep pressure CAP rate may diminish, but it never approaches zero (see Terzano and
Parrino 1991). Normally CAP sequences occur at sleep onset and after nocturnal
awakenings (Terzano et al. 1988). They are also abundant in S2 preceding REM sleep
(Terzano et al. 1985). This cyclic fluctuation of arousal is connected with alternations in
autonomic functions (e.g. heart rate, respiratory activity). In a recent study heart rate
variability related to CAP A phases resulted in a significant increase of low frequency
components with a decrease of high frequency components (Ferini-Strambi et al. 2000).
This reflects the shifting of the balance of the autonomic nervous system towards
sympathetic activation. This was the case even when the conventional arousals were
excluded.
CAP can be seen as a marker of instability in the sleep process. As the morphology of
phase A is the same for both external and internal stimuli, it can be seen as a response to
both internal and external messages (Terzano et al. 1988). CAP may be a sign of the
reorganisation of the brain so that it can respond to changes in environmental conditions
(Terzano et al. 1985). The cyclic, recurring character of CAP could suggest the existence
of natural arousal rhythm in the EEG of sleep.
To summarise, it seems evident that the macrodynamics of the sleep process consists of
continuously alternating microstates (Halász and Ujszászi 1991). These microstates form
naturally occurring oscillations and periodicities within sleep. Their internal relationships,
especially the possible connections between slow oscillation and CAP are still unclear.
17
2.3. STANDARDISED SCORING SYSTEM
2.3.1. Rules of the standardised scoring system
Since 1968 visual sleep stage scoring has been performed according to the guidelines
established by the committee led by Allan Rechtschaffen and Anthony Kales. The need for
adjustment of scoring rules was disclosed by the study of Monroe (1967) in which he
showed that the inter-rater agreement of scorings between different laboratories was low.
The aim of “A manual of standardised terminology, techniques and scoring system for
sleep stages of human subjects“ often called the “manual of Rechtschaffen and Kales“
(RKS), was to increase the comparability of results from different laboratories
(Rechtschaffen and Kales, 1968). The manual was based on the psychophysiological
knowledge of the sleep process at that time. It provides the minimum requirements for
polygraphic sleep studies of adult humans. It also provides the classification of sleep
stages, standardised terminology and definitions of different parameters obtained from
recordings.
The guidelines of RKS were designed for paper recordings. The manual contains
definitions for filters, gains, paper speed, pen deflection, and number of channels, etc. This
was very important because the settings affect amplitude, frequency and phase of
measured signals. Some of the requirements and instructions are no longer necessary
during recording with the increasing use of digital polysomnographs in which the
parameters can be adjusted afterwards.
A single optimal EEG derivation was specified for recordings; either the C4/A1 or C3/A2
can be used. Eye movement recording with two electrodes produces out-of-phase
deflections by eye movements as EEG activity in eye movement channels is seen as inphase deflections. Submental EMG is recorded to differentiate the decreased muscle tonus
in SREM. The minimum amount of channels that is required for one recording is four.
The definitions of the five sleep stages are essentially based on the stage classification of
Dement and Kleitman (1957). Sleep stages are characterised by a specific set of variables,
which are composed of different kinds of EEG patterns with eye movement and muscle
tonus patterns. Wakefulness stage (S0) consists of EEG alpha activity and/or low voltage,
mixed frequency activity. Stage 1 (S1) is defined by low voltage, mixed frequency EEG
without rapid eye movements. Vertex waves can also be seen. Stage 2 (S2) consists of 1214 Hz sleep spindles and KCs on a relatively low voltage, mixed frequency background.
The minimum duration for spindles and KCs was set at 0.5 s, and the amplitude demand
for KCs was 75 µV. Stage 3 (S3) consists of moderate amount (20 - 50%) of high
amplitude slow wave activity. The amplitude must exceed 75 µV, and the frequency of
that slow activity had to be < 2 Hz. Stage 4 (S4) consists of large amounts (> 50%) of high
amplitude slow wave activity. Stage REM (SREM) shows low voltage, mixed frequency
EEG with saw-tooth waves, episodic REMs and low amplitude submental EMG.
Movement time (MT) was defined as an additional score for the situations where amplifier
blocking or muscle activity in one epoch makes sleep staging impossible.
18
The most common parameters derived by sleep stage scoring are the time in bed (TIB),
sleep efficiency index (SEI), sleep onset latency (SOL) and the percentages of sleep stages.
Graphically the sleep process can be displayed as a sleep histogram, the hypnogram. Both
the numerical and graphic results are used for the differentiation between good and
disturbed sleep. They are also used as parameters in various psychophysiological and
pharmacological experiments.
The inter-scorer agreements for S2 and SREM have usually been quite high, whereas the
agreements for S1 and S3 have been much lower. Agreements found in different studies
have been 60 - 96 % for S0, 61 - 84 % for S1, 85 - 98 % for S2, 44 - 94 for S3 and 46 - 94
% for S4 depending on subject groups and whether the comparisons were made within or
between laboratories (Martin et al. 1972, Kuwahara et al. 1988, Kubicki et al. 1989, Kim
et al. 1992, Schaltenbrand et al. 1996). In a recent study among eight European sleep
laboratories the inter-scorer reliabilities (Cohen’s Kappa) for healthy subjects were 0.821
for wakefulness, 0.376 for S1, 0.759 for S2, 0.731 for SWS and 0.874 for SREM (Kunz et
al. 2000). For patients with different sleep disorders lower kappa values have been
obtained (0.723 for wakefulness, 0.295 for S1, 0.674 for S2, 0.676 for SWS and 0.82 for
SREM, Danker-Hopfe et al. 2000). The low inter-scorer agreements in visual sleep stage
scoring increase the variability and uncertainty of the results obtained by the method.
2.3.2. Evaluation of the standardised sleep stage scoring system
Scoring with epochs ignores short lasting oscillations
Sleep stage scoring in RKS is performed in epochs of equal lengths. The whole epoch is
scored as one stage. If signs of two or more stages are present the epoch is assigned to the
stage whose landmarks are of longest duration within the epoch. In standardised sleep
stage scoring the fluctuations of vigilance must be ignored. Epoch scoring enables the
convenient analysis of paper recordings. Results become easy to handle when one page is
scored into one stage. The one-page epoch also minimises the time used for later
parameter calculations. The chosen paper speed with an epoch length of 20 or 30 s can be
seen as a compromise between accuracy and laboriousness.
The hypnogram does not provide a proper description of the underlying sleep/wakefulness
continuum (Pardey et al. 1996a). With epoch scoring the 90-100 min. cyclicity is revealed,
but the shorter periodicities described above are not well visualised. The physiological
fluctuation of the vigilance states within an epoch remains unnoticed. Even if RKS scoring
is supplemented by arousal scoring (ASDA 1992) the microstructure of sleep is not
visualised properly (Terzano et al. 1985, Terzano and Parrino 1991). An excessively
smooth picture of the dynamics of the sleep process results from RKS.
Topographical aspects
With standard sleep scoring rules only central leads are taken for the classification. Using
only one EEG derivation means that only a minor part of the brain surface is evaluated.
The occipital localisation of the alpha activity was, however, pointed out already in 1949
(Brazier 1949). On the other hand Jasper and Andrews (1938) had paid attention to
19
another alpha activity, which was precentrally located and was not necessarily blocked
during light stimulation. The frequency of the wakefulness-related occipital alpha rhythm
is 8-12 Hz. More recent work has disclosed that the other alpha activity is an
approximately 2 Hz slower centro-frontal rhythm and is present during lower vigilance
state (Kubicki et al. 1985, Broughton and Hasan 1995). Theta activity reaches its
maximum in the central regions (Broughton and Hasan 1995). The amplitude of the delta
waves is highest frontally. Therefore the frontal delta waves can show S4 sleep pattern at
the same time as delta waves in central leads are too low to be scored as S4 (Kubicki et al.
1985).
The vertex waves, KCs and most often the sleep spindles are of higher amplitude in the
midline than in the C3-A2, C4-A1 derivations which are recommended in the manual
(Broughton and Hasan 1995). About one third of the KCs can be seen only by frontal
leads, whereas one third is seen by central leads and one third by both leads (Paiva and
Rosa 1991, McCormick et al. 1997). Two different types of sleep spindles have been
observed. The faster spindles which have a frequency of about 14 Hz are parietally
located, whereas somewhat slower 12 Hz spindles are frontal. Spindles recorded by the
recommended electrodes probably represent a mixture of spindle activity in the frontal and
the parietal area (Jobert et al. 1992). Some of the spindles are visible only parietally or
frontally (Kubicki et al. 1985) and are thus totally ignored by the standard system. Sawtooth waves show their voltage maximum at the vertex (Broughton and Hasan 1995).
Some arousals may be visible only frontally and are not visualised by the standard system
(O’Malley et al. 1996).
RKS does not take into account the continuity of the sleep process and phasic events
Scoring of S2 is heavily dependent on the presence of the phasic events, the spindles and
KCs. According to the standardised scoring rules S2 begins when the first well-defined
spindle or KC appears. However, no unambiguous definitions for the phasic events exist
(Broughton 1991).
The definition of the KC by morphology is difficult. KC is defined as a high-amplitude
(>75 µV) EEG pattern having negative sharp wave, which is immediately followed by a
positive wave. The start and endpoints of individual KCs are not clearly stated in any of
the definitions. In the manual of RKS the minimum duration of a KC is specified to be 0.5
s. However, Amzica and Steriade (1997) found the duration of KCs to vary from 0.250 s to
1.050 s. The minimum duration of a spindle has to be 0.5 s (Rechtschaffen and Kales
1968) but the minimum amplitude is not stated.
Usually the amplitude of spindles and KCs increases gradually at sleep onset. The decision
whether a spindle or KC is sufficiently well-defined is not easy and depends on the
experience of the scorer. The spindles and KCs represent sleep microstructure, but the
number and rate of these phasic events in no way affects sleep stage scoring unless there is
a longer than 3 min. pause in their appearance. But, as spindles are considered sleep
protective, their rate of occurrence may also have an impact on sleep quality.
20
S2 is easy to score as long as regularly occurring phasic events are present. Problems arise
when phasic events are sparse or when the amplitude of the background becomes higher.
By definition spindles and KCs should appear on a low voltage background activity. The
differentiation between S2 and S3 as well as the differentiation between S3 and S4 is made
by the percentages of slow wave activity within an epoch. This differentiation system can
be considered artificial (Lairy 1977). The amplitude of the slow wave activity must exceed
75 µV, which can cause problems with visual scoring. The issue of the amplitude criterion
was discussed by the Committee, but the criterion was upheld.
The 75-µV threshold seems to work very well with young people. Delta activity, however,
declines with age (Webb and Dreblow 1982, Feinberg et al. 1984). Therefore a high
amount of S2 and very low amounts of S3 and S4 are scored in the recordings of elderly
subjects even if they have no sleep complaints. As a consequence a hypnogram of a
healthy elderly person may resemble a hypnogram of a patient suffering from poor sleep,
for instance, insomnia or sleep apnea because S2 generally increases and S3 + 4 also
decreases in sleep disturbances (ICSD 1997, Figure 1). In RKS sleep often looks “more
normal“ than it in reality is and the actual pathological phenomena may be lost. It is
possible that, for example, completely different morphological states are scored as S2.
This was pointed out already in 1977 by Sträle (Lairy 1977). In apnea patients S2 could be
seen as a misleading term because the S2 of an apnea patient is constantly interrupted by
the periodic arousals and the microstructure of sleep looks quite different from the normal
S2 (Guilleminault et al. 1975).
RKS is not sufficient to describe drowsy states
The division into RKS stages ignores slight impairment of vigilance leaving drowsiness
states undefined. However, early studies already demonstrate different polygraphic
patterns associated with drowsiness. Simon and Emmons (1956) found a systematic
change in the alpha activity with decreasing vigilance; the amplitude of the fast
wakefulness alpha starts to decline with increasing drowsiness. This is followed by
occasional alpha attenuation. Further decrease in vigilance is associated with slowing
down of the frequency of the alpha activity by 2 Hz, later this slower alpha starts to
attenuate and finally disappears.
Hori et al. (1991, 1994) divided the conventional sleep stages S0, S1 and S2 into 9
different stages by EEG morphology. Stages 1-3 are separated from each other by the
amount of alpha activity, stage 4 consists of low voltage activity, stage 5 consists of
ripples, stage 6 is characterised by occasional vertex waves, in stage 7 vertex bursts are
seen, incomplete spindles appear in stage 8, and in stage 9 well formed spindles are seen.
Stages 1-2 correspond to wakefulness in RKS, stages 3-8 correspond to S1 and stage 9
corresponds to S2. The stages could be differentiated from each other by the auditive
reaction time test.
In RKS rules the vigilance state is determined mainly by EEG changes. SEMs are not
included in the rules although they are stated to be present in S1. With open eyes SEMs
with alpha activity can be regarded as a sign of lowered vigilance (Valley and Broughton
1983, Torsvall and Åkerstedt 1987). With closed eyes the appearance of SEMs can
21
precede the attenuation of the alpha activity by several seconds (Liberson and Liberson
1965). Kojima et al. (1981) divided vigilance into 4 different states based on the
frequency, distribution and continuity of the alpha waves. They observed that in alert
wakefulness REMs were more frequent, but with increasing slowing down of the
frequency and widening of the distribution as well as decreasing of the continuity of the
alpha activity, SEMs became more common and REMs were reduced. The appearance of
SEMs with alpha activity can be taken as an early sign of drowsiness also with closed eyes
(Santamaria and Chiappa 1987, Ogilvie et al. 1988).
Figure 1. Sleep disorders cause non-specific changes of hypnograms scored by RKS. Therefore the nature of the disorder
cannot always be identified by the hypnogram alone. It may also be difficult to differentiate between patients and healthy
elderly subjects without sleep complaints.
At the top a hypnogram of a young patient with sleep apnea syndrome. He also suffers from insomnia.
In the middle and at the bottom hypnograms of healthy well-sleeping elderly subjects without sleep complaints.
The patient seems to sleep quite continuously, presenting no slow wave sleep. In the middle the elderly subject has
practically no slow wave sleep. The subject on the bottom has slow wave sleep, but also frequent awakenings
fragmenting sleep.
22
Atypical PSG patterns
The rules of RKS are suitable for normal, common sleep EEG characteristics. With
abnormal or deviant normal electrophysiological patterns problems with scoring arise.
Alpha-delta sleep is defined by the persistence of alpha activity during NREM sleep (Hauri
and Hawkins 1973). Moldofsky (1990) has suggested that the alpha-delta pattern is a
reflection of nonrestorative sleep of fibromyalgia patients but it can also been seen in
healthy good sleepers (see Pivik and Harman 1995). As there are no rules for scoring this
pattern it is unclear how it should be classified. If alpha activity during sleep is abundant it
may considerably hamper visual scoring. The determination of sleep onset, arousals and
longer wakefulness episodes within sleep may prove difficult. With low-alpha subjects the
problem is the reverse: the attenuation of alpha activity cannot be used as a marker of S1
and one has to rely on the theta activity.
In parasomnias EEG sleep patterns, even delta waves, can be seen with concomitant body
motility, high EMG tonus and sometimes with eye movements (Broughton 1993, Schenck
et al. 1998). In the standard scoring system there are no rules for scoring such a state. The
epochs with parasomnias might be scored as wakefulness, movement time or one of the
NREM stages, depending on the scorer and the electrophysiological picture.
In narcolepsy elevated submental muscle tonus can be seen during REM sleep (Schenck
and Mahowald 1992). Scoring such cases the state with SREM alike EEG with persistent
muscle tonus could be wrongly indexed as wakefulness or S1. On the other hand the onset
of SREM periods could be delayed if no decrease in submental EMG occurred in epochs
with REM like EEG preceding the appearance of the first REMs (Kubicki et al. 1985).
REM sleep behavior disorder is characterised by elevated submental muscle tonus and
excessive chin or limb movements (for a recent review see Sforza et al. 1997).
Corresponding scoring problems to those of recordings from subjects with narcolepsy and
parasomnia may emerge. The REM sleep episodes could be scored as wakefulness, SREM
or sometimes partly as S1.
Especially during the first SREM period both SREM and S2 related phasic events may
occur quite close to each other. This phenomenon was already described by Dement and
Kleitman (1957), who called it "slipping" down from SREM to S2. It can be seen in 1-8%
of normal subjects (Broughton 1993). The reason for this phenomenon might be a very
rapid alternation of stages, but the other possibility is that SREM and NREM related brain
processes could coexist. With a long standard epoch the scoring of these episodes becomes
difficult.
Summary of the criticism of RKS
Some of the shortcomings of the standardised scoring system can be summarised as
follows. Scoring with the RKS rules in many cases requires subjective judgement of the
EEG, which can lead to unreliable results. Due to epoch-thinking all short-lived changes in
the signal are masked although they could be the most interesting from the clinical point of
view (Pardey et al. 1996a). Also, if epoch-scored vigilance states are used together with a
23
reaction time test, misleading results may be obtained (Ogilvie and Wilkinson 1988,
Conradt et al. 1999).
The EEG derivations recommended for standardised visual scoring are not optimal for any
of the EEG events needed for sleep staging. Many events may be poorly visible even in
normals because of their location. This may partly explain why differences in sleep
parameters between healthy subjects and patients remain modest. As it is now, the
parameters obtained by sleep stage scoring give a superficial picture of the differences
between good and poor sleep. In addition to the need for more EEG derivations, a more
detailed division of the sleep stages seems possible by EEG morphology.
This is entirely in line with the opinion expressed by the Committee led by Rechtschaffen
and Kales. They encouraged the use of other concepts wherever needed. Revisions of the
manual were suggested with the acknowledgement of new information. However, there
have been no systematic attempts to revise the rules.
2.3.3. Visual methods to supplement and improve RKS
An occipital derivation can be used to record alpha activity in order to improve the
differentiation between wakefulness and S1. According to Williams et al. (1974 p. 21-22)
this is even necessary in most circumstances. They also recommended the use of a frontal
channel for a more accurate detection of delta activity.
RKS was not designed to address sleep disrupted by respiratory events. Wakefulness and
sleep cannot be accurately estimated and total sleep time (TST) and apnea indices tend to
become unsatisfactory. To avoid the problem of classifying fragmented sleep into normal
sleep stages, transitional sleep (T-sleep) was defined (McGregor et al. 1992). T-sleep is
used to separate the continuously alternating sleep and wake transitions of sleep apnea
patients from normal sleep. T-sleep differentiates well between the periods of disrupted
sleep and more normal sleep, but with T-sleep scoring the microstructure of disturbed
sleep remains undescribed.
The arbitrariness of the rules separating S2, S3 and S4 could be avoided if NREM sleep
were be classified from the viewpoint of the spindles into spindle-dominant and deltadominant sleep (Naitoh et al. 1982). This could be easily done even visually. With this
kind of scoring the microstructure of the sleep process would be better revealed than by
RKS.
Use of shorter epoch lengths can improve the temporal resolution of scorings. Shorter
epochs have been used often in vigilance studies (Morrell 1966, Häkkinen 1972,
Townsend and Johnson 1979, Valley and Broughton 1983, Belayavin and Wright 1987,
Torsvall and Åkerstedt 1987, Värri et al. 1992) but seldom in night recordings (Weitzman
et al. 1980).
Additional scoring of arousals can reveal sleep fragmentation. Arousals are more frequent
in disturbed sleep (Staedt et al. 1993). But as sleep consists of different periodicities, it is
somewhat unclear what can be considered as normal fragmentation and when the
24
fragmentation is excessive (Martin et al. 1997). The ASDA arousal criteria (1992) can be
seen, however, to be quite conservative and some patterns resembling arousals are thus
omitted from calculations. As the minimum duration of the arousals must be at least 3 s,
some short arousals are ignored. The inter-scorer agreement for arousal scoring is not
always satisfactory (73% - 99%), which further increases the problem of arousal scoring
(Stepanski et al. 1984, Roehrs et al. 1994, Sangal et al. 1997).
Among the different visual methods, CAP scoring has proven the most successful. It has
revealed a more fluctuating picture of the sleep process than can be obtained by the
conventional scoring method. It has also provided a more specific description of normal
sleep and sleep disturbances than RKS. CAP rate is increased by artificial sleep disruption
such as by noise (Terzano and Parrino 1993). It is also clearly enhanced in sleep
disturbances as in sleep apnea, periodic limb movements and insomnia (Terzano et al.
1996, Parrino et al. 1997). In insomniacs CAP scoring has turned out to be an efficient tool
in drug experiments. Whereas conventional sleep parameters supply only limited
information, the CAP parameters (microstructure) have shown high sensitivity to both
drug and condition factors (Parrino et al. 1997).
2.4. COMPUTERIZED SLEEP ANALYSIS
2.4.1. Sleep stage scoring
There have been attempts to develop automatic methods for sleep scoring for nearly 30
years. So far the only method that has been widely applied in sleep research is the hybrid
system developed by Gaillard and Tissot (1973). The use of the system requires that all
critical parts, for instance, sleep onset and stage shifts have to be re-examined afterwards
by a visual scorer. On the other hand with this system the differentiation between S2, S3
and S4 is totally based on the computer program. One of the pioneers in the field is Smith
and co-workers (Smith et al. 1975, Smith et al. 1978). In practice, the majority of even
novel digital systems are based on the hybrid principles introduced by Gaillard and Tissot
(1973) and Smith et al. (1978). The method implies that there are detectors for similar
waveforms that are used in human scoring, for instance, spindles, delta activity and eye
movements. The computer program combines the information obtained by the detectors
and forms the stages. In young, healthy subjects the total agreement between visual and
computer scoring has been found to be 73 – 91 %. Agreements for wakefulness have
varied 28 – 93 % and for S1 22 – 95 %. Agreements from 45 to 97 % have been obtained
for S2, 50 – 91 % for S3, 55 – 95 % for S4 and 69 – 94 % for SREM (Martin et al. 1972,
Gaillard and Tissot 1973, Kumar 1977, Smith et al. 1978, Hoffmann et al. 1984,
Kuwahara et al. 1988, Kubicki et al. 1989, Schaltenbrand et al. 1996). There are few
studies with man/machine agreement in subjects with sleep disorders. Lower total
agreements, between 63.1 % and 70.5 % have been obtained than in subjects without sleep
complaints (Hasan 1983, Sangal et al. 1997).
25
In general, automatic analysis is not considered sufficiently reliable for clinical use but the
results have always to be re-examined by a visual scorer. It has been postulated that the
problems of computerized sleep analysis are not due to the inadequacy of computer
programming, but to the ambiguous definitions of sleep stages (Hasan 1996). More
specific methods that follow the sleep process more closely are required. A step in this
direction is the automatic detection of CAP, which seems promising (Rosa et al. 1999).
2.4.2. Spectral analysis
Automatic analysis systems can also be used to quantify different phenomena derived from
polygraphic recordings. Among these methods the Fast Fourier Transform (FFT, Cooley
and Tukey 1965) is evidently the most commonly used. FFT has been used both for
attempts at computerized sleep stage scoring and as an independent tool for quantitative
studies on sleep structure and dynamics.
Almost 60 years ago Knott et al. (1942) presented their data with spectral power, which
they called energy. They compared the energy of different frequency bands in wakefulness
and in three sleep stages (low-voltage, spindles and spindles + random) following the
criteria of Loomis et al. (1937). They noted the negative relationship between the activity
in the 1-3 Hz and 8-12 Hz bands. Their conclusion was that there are changes in energy
between wakefulness and sleep but no new frequencies appear in sleep as compared to
wakefulness. However, a characteristic sigma peak during the two deeper sleep stages is
clearly visible in their figures.
The first spectral study of the sleep stages defined according to the Dement and Kleitman
(1957) criteria was made by the group of Johnson (1969). The study was based on clearly
defined, artifact-free 1 min. sections from each stage. They found an average peak
frequency of alpha activity in wakefulness of 9.8 Hz. They also found a slower alpha peak
in stage 1, although this is not clearly visible in their figure with the spectra. A sigma peak
was stated to exist in all sleep stages but not in wakefulness. According to their view
sigma activity was unique to sleep. In this respect they disagreed with Knott et al. (1942).
The power of theta and delta activity increased with deepening of sleep but a large amount
of slow activity was found in all stages.
In a subsequent study the group made an attempt to discriminate between sleep stages
scored according to Dement and Kleitman (1957) by EEG spectra (Lubin et al. 1969). The
spectra derived by FFT from 1 min. sections for each stage were divided into 5 frequency
bands: delta, theta, alpha, sigma, beta. Linear discrimination among the six stages by
stepwise multiple regression was used in order to find a minimum set of EEG predictors
that would give the best possible multiple correlation. The poorest result was obtained for
stage 3, where the linear discriminator was almost imperceptible. Linear discrimination
between selected cases gave better results but stage 1 and stage REM could still not be
separated. The separation between stages 2, 3 and 4 was also unsatisfactory. It was
suggested that this poor result was due to unreliability in visual scoring, which should
therefore be replaced by quantitative criteria. It was also stated that the detection of phasic
events might improve the results.
26
Later on several attempts were made to use spectral analysis as a basis for the
discrimination between sleep stages scored according to RKS. One of the first attempts to
develop a computer program for automatic sleep stage scoring of a whole-night recording
was made by Martin et al. (1972). They first tried to apply spectral analysis to distinguish
between S2, S3 and S4. As the results were poor they had to develop a specific computer
algorithm for the detection of high-amplitude delta waves in order to replicate visual
scoring. However, they believed that quantitative measurement of continuous slow wave
activity was a more reliable and sensitive indicator of changes in slow wave sleep than the
division into discrete stages.
Molinari et al. (1984) used stepwise linear discriminant analysis of spectral parameters to
distinguish between RKS sleep stages. The parameters used varied between subjects. The
error rate was only 10-15 %. Analysis showed that most errors occurred between
wakefulness, S1 and SREM or between adjacent slow wave sleep stages. Molinari et al.
suggested that in order to obtain a more precise description of the sleep process the
excessively rough visual staging of NREM sleep should be replaced by a continuous
model. The weakness of the method is that the same set of parameters was not applied for
all subjects.
A well-known problem in using FFT is that the method can give a higher spectral power
for a large number of low-amplitude slow waves (< 75 µV) than for a few waves with an
amplitude exceeding the 75-µV scoring criteria of the standard manual. FFT is, however,
considered suitable for the quantitative study of the dynamics of the sleep EEG (Borbély et
al. 1981). Already Martin et al. (1972) suggested that spectral analysis can provide an
efficient an reliable tool for various sleep studies. Effects of sex and ageing on sleep EEG
have been studied by FFT (Ehlers et al. 1998). Besides sleep macrostructure spectral
features of arousals have been studied by FFT. Halász and Ujszászi (1991) noted that in
NREM sleep an acoustic stimulus elicits a temporary increase in the power of nearly all
frequency bands except for the 13-14 Hz band in which a depression can be seen.
2.4.3. FFT in sleep-wake transition
Several groups have used FFT to study the changes in spectral power during sleep onset.
Spectral analysis provides a method to examine the microstructure by tracking spectral
changes throughout the entry into sleep as well as in smaller vigilance changes during the
day. EEG total power and the power in the 6.25-9 Hz band have been shown to increase
during sustained wakefulness (Cajochen et al. 1995, Corsi-Cabrera et al. 1996).
A general increase in EEG total power as well as increases in all frequency bands have
been observed at sleep onset with closed eyes (Ogilvie et al. 1991, Ogilvie and Simons
1992). There are two kinds of observations regarding alpha activity: decreases in alpha
band power occur prior to visually scored sleep onset (Badia et al. 1994). On the other
hand increases in alpha power band have been observed at sleep onset (Ogilvie et al.
1991). In another study by the same group the highest levels of alpha activity were shown
to occur during the most alert responsiveness and during early behavioral sleep (Ogilvie
and Simons 1992). Increase in theta band power has been observed already before S1 can
be scored according to the conventional scoring criteria (Badia et al. 1994). A decrease in
27
the power of beta activity has been found to occur with the shift from wakefulness to S1
(Wright et al. 1995).
Topographical studies by FFT
Buchsbaum et al. (1982) studied sleep during daytime naps with 16 EEG channels. Delta
power was highest in the midline. There were neither regional changes nor sleep state
changes for theta activity. Alpha activity was greatest parieto-occipitally in restful waking
with eyes closed. In slow wave sleep a frontal alpha dominance was observed.
Topographic beta effects were minor. The data suggested that traditional sleep stages
differ in the quantitative and topographic distribution of EEG frequency bands. Zeitlhofer
et al. (1993) in their study on 10 young healthy volunteers without sleep complaints
observed essentially similar results.
Walsleben et al. (1993) studied whether topographic brain mapping could provide
additional information for the detection of EEG changes associated with sleep apneas.
They concentrated on S2, where theta and slow alpha activity frequently occurred in the
frontal areas not clearly visible in the standard electrode sites used for RKS. The activity
diminished during apnea. This diminution was related to the severity of the apnea. After
the apnea the activity returned to baseline. Earlier Svanborg and Guilleminault (1990) had
observed fast increases of delta activity during apnea in S1 and S2. This increase was
either uniformly distributed or showed a post-central maximum. Just before the cessation
of the apnea a strong increase in fast activity was found, indicating arousal. The rapid
increase in delta activity may be caused by a very fast transition of vigilance into deeper
stages of sleep.
According to Wright et al. (1995) the greatest changes of EEG power occur in the
posterior areas. Before sleep onset wakefulness alpha activity is located occipitally but
alpha can also be seen in central regions. In drowsiness a more widespread topographic
distribution over all the brain areas except in the temporal regions has been observed
(Cantero et al. 1999). At sleep onset alpha power tends to decrease occipitally more than in
other regions (Badia et al. 1994). In one of his early works Hori (1985) found that the
alpha power decreased significantly 2 min. after onset of S1. The maximum difference in
the disappearance of alpha activity between the different midline EEG derivations was
approximately 15 s. After this a low-voltage pattern without a clear dominant peak was
seen. At the onset of S1 the mean delta and theta power started to increase rapidly. The
latency of increase in delta power was approximately 5 min. The increase in delta power
was slower and slightest occipitally. Theta appeared first in Fz, Cz and Pz with a latency of
approximately 3 min. and later in Fpz and Oz. Sigma activity also appeared earlier in Fz,
Cz and Pz than in the frontopolar and occipital locations. The latency of appearance was 5
min. No significant regional differences in beta power were observed. Wright et al. (1995)
also noted that theta increased from wakefulness to sleep in the midline sites. The greatest
power change occurred in the 3-4 Hz band at the vertex. Lower frequency delta bands were
not included in the study.
28
Tanaka et al. (1997) studied the topography of Hori’s 9 stages. The maps showed the
dominant areas of alpha activity to move along the midline of the scalp from the posterior
areas to the anterior areas. The parieto-occipital alpha2 activity during wakefulness (stages
1 and 2) decreased markedly at stages 4 and 5, increasing in the frontal areas from stage 7.
Sigma band activity did not show significant changes until stage 6, where a dominant
sigma focus appeared in the parietal region. The sigma power increased sharply in stage 8.
The dominant area of delta and theta band powers was first observed in the frontal region
extending from the central to the temporal regions as a function of stage. The activities of
delta -, theta -, and sigma bands did not show topographical changes but these activities
developed in their focus areas.
2.5. ADAPTIVE SEGMENTATION
2.5.1. Automatic adaptive segmentation
EEG is not a stationary signal; its frequency and amplitude vary continuously. However,
most quantitative methods used in EEG studies, including FFT, assume that the signal in
the period analysed is stationary (Barlow 1985, Pardey et al. 1996a, Pardey et al. 1996b).
The most common way to solve the problem of nonstationarity has been to use short,
quasi-stationary segments. Usually the segments have been of equal length. Consequently
the rapidly changing EEG signal cannot be completely stationary within the segments. In
the FFT work presented above the fixed segment lengths have varied between 2.5 s and 60
s. With paper recordings it is a practical necessity to use long epochs of equal length for
analysis. However, with digital visual or computer analysis the epoch boundaries could be
placed freely. Barlow (1985) has stated that to study short nonstationarities a segment
length of 1 s or less is necessary.
The use of short segments is likewise not unproblematic. Analysis of S2 or SWS in 1 s
epochs would cause artificial segmentation. For example, some segments would contain
phasic events (spindles, KCs), whereas some would not. This was already pointed out by
Dement and Kleitman (1957). In general, sleep stages consist of the combination of
background activity and the phasic events. With a short segment these would be separated.
In order to form a stage entity these would have to be combined to retrieve a meaningful
result. On the other hand, use of short epoch does not remove the problem that the
boundaries do not coincide with the changes in EEG activity.
A second way to solve the problem of nonstationarities in the EEG is adaptive
segmentation. In adaptive segmentation no fixed time period is used. Praetorius et al.
(1977) were the first to apply automatic adaptive segmentation to EEG analysis. The
system was originally developed to the analysis of clinical EEGs. The EEG stage boundary
is indicated when there is a change in the EEG pattern. Usually a reference window and a
moving test window are used. When a threshold based on the magnitude of the amplitude
and frequency changes is exceeded, a segment boundary is created and a new segment is
taken as the reference window. The procedure is then repeated.
29
Automatic adaptive segmentation procedure was found quite satisfactory (Creutzfeldt et al.
1985). The use of automatic adaptive EEG segmentation minimised human bias in the
selection of portions of EEG recordings for automatic analysis (Barlow et al. 1981). In the
work of Creutzfeldt et al. (1985) changes of vigilance were also well represented. Four
clusters of different vigilance states were found: alpha, mixed-frequency (low voltage –
theta alternating), spindles and delta. The boundaries set by the computer program
corresponded well to the visual estimation of epoch boundaries.
In the earlier work of Barlow et al. (1981) only one channel was used for segmentation. In
subsequent work it was stated that segmentation of even 8-12 channels could be feasible
(Creutzfeldt et al. 1985). However, in order to avoid underclustering it was recommended
that only 2 channels, the homologous pairs at each side be segmented simultaneously, after
which different pairs be analysed separately.
Another group which has applied adaptive segmentation of EEG in sleep research, was
Gath and Bar-On (1985). The segments were classified into six clusters. In the opinion of
these writers the division of the sleep EEG into segments of variable length took better
account of the variations and continuity of the signal.
In Tampere automatic adaptive segmentation has been applied in vigilance study. In a first
application the EEG of multiple sleep latency test (MSLT) naps were automatically
segmented (Hasan et al. 1993). One parieto-occipital EEG-lead was used for the
segmentation procedure (P4-O2). As a result mainly short segments of 0.5 – 2 s were
obtained. More stages for wakefulness and drowsiness were used than in the standard
scoring system and eye movements were also taken into account. The stages were: WM for
artifacts and increased muscle and movement activity, WO for mixed-frequency EEG with
fast eye movements, WC for posterior alpha rhythm with no eye movements, D for
posterior alpha activity with SEMs or low-amplitude EEG with SEMs or no eye
movements, S1 for increased theta activity and no fast eye movements, S2 for sigma
activity and SREM for REM sleep periods. The stages WM, WO, WC correspond to
wakefulness in the conventional scoring criteria. Stage D corresponds to wakefulness
(alpha activity with SEMs) and partly to S1. S2 corresponds to S2, and SREM corresponds
to SREM in the RKS rules. The segments were scored into stages both by computer
analysis and visually. The method was further developed by applying it to the analysis of
ambulatory recorded daytime polygraphic data (Hirvonen et al. 1997).
As only one EEG channel was used for both segmentation procedure and for visual
analysis, topographical aspects could not adequately be taken into account. The manmachine agreements were satisfactory (70 – 79 %) especially in high-alpha subjects. The
agreements in low-alpha subjects were 64 – 70 % (Hasan et al. 1993). The automatic
segmentation procedure was never validated against visual adaptive segmentation.
The same segmentation procedure with visual stage classification has later been used in
two reaction time test studies with sleep apnea patients. The classification system with
more stages and short segments gave a more accurate description of vigilance fluctuations
than RKS (Conradt et al. 1999). In a subsequent work the stages were found to correlate
with reaction time (Kinnari et al. 2000).
30
2.5.2. Visual adaptive segmentation
To the best knowledge of the author the first attempt to visually divide sleep recordings
into electrophysiologically homogenous segments is the work of Himanen et al. (1999). In
that preliminary work of visual adaptive segmentation (VASS) the signal was both
segmented and scored manually. Stages represented different frequencies and amplitudes
and different kinds of eye movements were also taken into account. The stages were
Wake-low for low-voltage mixed frequency EEG with fast eye movements or no eye
movements. Wake-alpha was defined as posterior alpha and no eye movements. Drowsyalpha consisted of posterior or diffuse alpha activity with any kind of eye movements or
frontal alpha activity with SEMs. Drowsy-low consisted of low-voltage mixed frequency
EEG with SEMs. Theta activity had to be present in S1VASS and spindles or KCs in
S2VASS. Arousals were scored separately as were periods with increased EMG activity or
movements. Stages Wake-low, Wake-alpha and Drowsy-alpha correspond to wakefulness
in the standard scoring system, and Drowsy-low and S1VASS correspond to S1. S2VASS
corresponds to S2. In visual analysis it is easier to score on the basis of several channels
than in automatic analysis. However, even if the aim was to take into account the
topography of the EEG, it turned out that this division into stages did not separate
electrophysiological stages into proper categories. It was also concluded that in future
studies SEMs should be separated more distinctly from other eye movements.
2.6. MULTIPLE SLEEP LATENCY TEST IN VIGILANCE STUDY
2.6.1. Definition of MSLT
The MSLT is a daytime polygraphic study that is widely used in clinical practice to
quantify daytime sleepiness (Carskadon 1993). It can be considered as the gold standard
for the objective measurement of sleepiness because it is generally agreed to be valid and
reliable (Johns 2000). There are also reference values for the assessment of normality. The
MSLT consists of four or five tests performed at two-hour intervals. In the clinical form of
the test patients are allowed to try to sleep for 20 min. If sleep occurs the test is continued
for 15 min. to detect potential sleep onset REM periods. The maximum duration of the test
is thus 35 min. Fast sleep onset parallels greater sleep tendency and slower sleep onset
greater alertness. Sleep onset is defined as the first 30 s epoch scored as S1 (Carskadon
1986). The average sleep latency for the four or five latency tests is the most common
parameter used to express the level of sleepiness. The average sleep latency of < 5 min. is
generally considered pathological, indicating increased sleep propensity (Richardson et al.
1978, van den Hoed et al. 1981). Values 5 – 10 min. are considered borderline scores, and
values > 10 min. are considered normal.
31
2.6.2. Problem of sleep onset
Subjective perception of being asleep is of little value in determining sleep onset. It has
been shown that the feeling of having slept corresponds poorly with psychophysiological
measurements (see Pivik 1991). On the other hand it has been shown that subjective
reports of having been asleep increase with deepening of sleep from S1 to S3+S4 (see
Agnew and Webb 1972).
Sleep onset can be defined by behavioral and EEG criteria. If an EEG criterion, for
instance, MSLT latency, is used to define the onset of sleep, an adequate definition of
sleep onset is needed. Since the tendency to sleep rather than the amount or maintenance
of sleep is the variable of interest in the MSLT, the first epoch of S1 has been chosen as
the marker of sleep onset. In addition the latency to S1 has been found to be associated
with performance decrement (Carskadon and Dement 1979, Carskadon et al. 1981).
Because sleep apnea patients have difficulties in staying asleep for more than 20-30 s use
of one S1 epoch is justified (Carskadon and Dement 1979). If the latency to the first S2
epoch was used the mean latencies of patients with narcolepsy became borderline and
sleep apnea patients had almost normal values (Browman and Winslow 1989).
Moment of sleep onset by EEG, subjective assessment, reaction times and hypnagogic
imageries
The moment of sleep onset was already investigated by Davis et al. (1937). With stimulus
experiments the transition, subjective “floating state“, from wakefulness to “real sleep“
was located in the B - state with low voltage EEG activity. This already took place after a
5 s interruption in the alpha rhythm. State C with spindles was considered definite “real
sleep“. Gastaut and Broughton (1965) noted that hypnagogic imageries were most intense
in stage 1B with loss of alpha activity. They were already present in stage 1A with slowing
and diffusion of alpha rhythm and disappeared in phase 2.
Hori and his group (Hori et al. 1994) using their 9-stage classification noted that most
hypnagogic imageries were remembered in the theta stage (stage 5). These imageries were
least frequent in stages 1 and 2 with alpha activity. When the 9 stages were grouped
according to the subjective assessment of behavioral state, reaction time and recall rate of
hypnagogic imageries the subgroups were not completely coincident with each other. This
shows that the definition of sleep onset is dependent on the method of determination. As
the proportion of subjective responses of having been asleep was only 43.7 % in
conventional S2 the sleep onset period could be considered to extend beyond S1.
If behavioral sleep is defined as lack of responses then S4 would be the only true sleep
stage with no responses. On the other hand, response failures can also be seen during
wakefulness. If the criterion for wakefulness is cognitive response to external stimulation,
then only S3 and S4 and SREM can distinguish between true sleep and wakefulness, as
cognitive responses are possible in S1 and S2. It seems that the presence or absence of the
response is an uncertain measure of sleep onset. Slowing of the reaction time should be
used as an additional parameter (Ogilvie and Wilkinson 1988).
32
S1 is a stage which is neither simple wakefulness nor sleep. In terms of behaviorally
defined sleep and wakefulness both have been found to be almost equally likely to be
present in S1 (Johnson 1973, Ogilvie and Wilkinson 1988). On the other hand, it has been
shown by reaction time tests that S1 differs from wakefulness in response rate and latency
(Ogilvie et al. 1989).
S1 can be seen as a transition period with fluctuating nature between wakefulness and
sleep. Slower and less frequent reaction time test response has been obtained in S2 than in
S1 (Ogilvie et al. 1989). S2 is in general thought to represent true sleep (Agnew and Webb
1972, Johnson 1973, Webb 1986). Ogilvie has suggested that the transition from
wakefulness to sleep should be dealt with as a “sleep onset period“ instead of dividing it
into discrete stages (Ogilvie et al. 1989).
SEMs at sleep onset
Although the EEG can be considered an important indicator of vigilance, eye movements,
especially SEMs, are also significant. This was pointed out already by Miles (1929).
Rechtschaffen and Foulkes (1965) conducted an experiment where they kept the eyes of
the sleeping subjects open. When objects were presented during alpha with SEMs there
was no recall of the objects.
Hori (1982) observed that SEMs are concomitant with a process preceding or initiating
drowsiness and that they disappear once drowsiness has reached a certain level. The same
phenomenon was observed by Ogilvie et al. (1988), who studied reaction times and SEMs.
They noticed that SEMs were absent when response rate was fast, with moderate response
rates SEMs were more prevalent, diminishing with very slow responses. In S2 and with
behavioral sleep determined by response failures SEMs were absent. On the other hand
SEMs have been observed in wakefulness with closed eyes (Shimazono et al. 1965).
Summarising the definition of sleep onset
As sleep onset is difficult to define in terms of a single parameter or stage the use of
several parameters gives more confidence. Many studies have been performed by
comparing only one electrophysiological and one behavioral or performance parameter or
two electrophysiological parameters. It would be more conclusive to use a combination of
EEG, SEMs, muscle tonus and performance with additional autonomic nervous system
measurements such as the respiration pattern which is unstable at sleep onset and stabilises
in S2 (Ogilvie and Wilkinson 1984, Ogilvie et al. 1988).
If EEG, EMG and eye movements (EOG) explain what sleep is, they should correlate with
behavioral definitions of sleep. Relevant behavioral changes at sleep onset are, however,
not synchronised. It is clear that it is impossible to define an unambiguous moment of
sleep by any combination of parameters that would suit all needs and conditions. The
temporal dispersion in the signs of falling asleep makes the task even more difficult.
Therefore, as Rechtschaffen (1994) has emphasised, one should focus on the purpose and
utilisation of the definition. If the aim is to track the first moments of vigilance impairment
then very early signs of drowsiness, for instance, changes in the alpha activity and slowing
33
of eye movements should be considered. If, on the other hand, one is interested in knowing
when the subject is definitely asleep then the disappearance of responses and the
appearances of sleep spindles should be noted.
2.7. VALUE OF MSLT IN DETERMINING SLEEPINESS
2.7.1. Positive findings
Conflicting results have often been obtained when correlations between MSLT scores and
subjective sleepiness or performance impairment have been studied. Sleep deprivation
studies have shown clear correlations between MSLT and the duration of deprivation,
subjective sleepiness or performance (Carskadon and Dement 1981, Carskadon et al. 1981,
Carskadon and Dement 1979, Borbély et al. 1985). However, it was later concluded that
only very low MSLT scores had a relationship to performance (Carskadon and Dement
1982).
In clinical work narcoleptic patients can in general be distinguished from patients with
other sleep disturbances and normal controls by the MSLT (Richardson et al. 1978, van
den Hoed et al. 1981). In patients with suspected excessive daytime somnolence (EDS),
subjective sleepiness measured by Epworth Sleepiness Scale (ESS) correlated negatively,
but not strongly (correlation coefficient –0.37), with MSLT scores. ESS scores of 14 and
above (range 0 – 24) predicted a low mean sleep latency on the MSLT (Chervin et al.
1997).
2.7.2. Negative findings
Subjective sleepiness or difficulties in falling asleep do not necessarily correlate with the
MSLT latency (Chervin et al. 1995). Mean sleep latencies between 5 and 10 min. and
sometimes even lower scores can be found in subjects without sleep complaints (Roth et
al. 1980, Manni et al. 1991, Harrison and Horne 1996a, Geisler et al. 1998). Carskadon
and Dement (1979) found a baseline mean sleep onset latency of 5.4 min. in healthy
students.
In a study on a patient population with sleep apnea, periodic limb movement syndrome
(PLMS) and complaints of insufficient sleep, no correlations between MSLT scores and
subjective sleepiness were obtained (Pressman and Fry 1989).
In insomniacs and normal controls no significant relationships between subjective
sleepiness and MSLT scores were found (Seidel et al. 1984). No correlations were found
between MSLT and personal inventory scores or tension and anxiety. In the control group
slower card sorting was associated with shorter MSLT latencies.
34
In the extensive study by Johnson et al. (1990) no correlations between MSLT and
performance or mood ratings in normal subjects were obtained. In a subsequent study
MSLT and subjective sleepiness were significantly correlated at 06:00h, but the correlation
became nonsignificant as the day progressed (Johnson et al. 1991). Correlations between
lapses in tapping-task and subjective sleepiness were generally nonsignificant. MSLT and
tapping-task were correlated in the group as a whole but not in all subgroups. According to
the study a 5 min. mean MSLT latency did not necessarily indicate EDS but could also be
a sign of a good sleeper.
Relatively short MSLT latencies have been obtained with normal scores in a psychological
task sensitive to sleepiness (Harrison and Horne 1996a). In a study on elderly subjects
MSLT-defined alertness/sleepiness was unrelated to neuropsychological test results
(Bliwise et al. 1991).
In a recent study the sensitivity and specificity of ESS, MSLT and maintenance of
wakefulness test (MWT) were compared (Johns 2000). MSLT was found to be the least
discriminating test of daytime sleepiness between narcoleptic and normal subjects. The use
of MSLT as a gold standard was strongly criticised.
2.7.3. MSLT and nocturnal polygraphic parameters
Variable results have been obtained when parameters derived from night polygraphies
(PSG) have been correlated with MSLT scores. In a clinical population study the only
nocturnal parameter having a significant correlation with the MSLT score was sleep onset
latency of the PSG (Chervin et al. 1995). However, the correlation coefficient even for this
variable was only 0.45. The correlation coefficients for demographic or other PSG
variables including TIB, SEI, TST, minutes and percentages of sleep stages, wake after
sleep onset, number of awakenings, respiratory disturbance index and lowest SaO2 were
between -0.22 and 0.15.
In a study on clinical patients MSLT latency had positive correlations with short PSG
latency and the amount of stages 3-4 (Guilleminault et al. 1988). There was also a negative
correlation between the MSLT score and the amount of nocturnal S1. Roehrs et al. (1989)
studied correlations between MSLT latency and respiratory parameters and arousals. The
correlation coefficients found were all between –0.33 and 0.08.
Van den Hoed et al. (1981) studied EDS patients suffering from various sleep disorders
having short (< 5 min.), moderately long (5-11 min.) and long (> 11 min.) MSLT
latencies. In general patients with short MSLT latencies had short sleep latencies at night,
shorter sleep cycles, higher sleep efficiency and earlier REM sleep than the patients with
long MSLT latencies.
Stepanski et al. (1984) found a 0.48 correlation coefficient between the total number of
arousals and sleepiness in MSLT. However, in closer analysis of different subject groups
(apnea, PLMS, normals) the correlation coefficients remained under 0.33. Insomnia
patients showed a negative correlation (-0.50) between MSLT defined sleepiness and
arousals.
35
Experimental nocturnal sleep fragmentation has been shown to cause a statistically
significant reduction in the MSLT latency on the following day (Roehrs et al. 1994).
Latencies changed from normal to almost borderline scores. The dispersion was, however,
considerable. In the same year, Philip et al. (1994) obtained similar results.
2.7.4. MSLT in sleep apnea syndrome
Obstructive sleep apneas consist of repetitive cessations of airflow caused by obstruction
in the upper airways. The obstructive sleep apnea syndrome (OSAS) consists of both
polygraphic and clinical findings. The diagnostic criteria proposed by the International
Classification of Sleep Disorders are presented in Table 1. Apneas and hypopneas cause
sleep fragmentation (Isono and Remmers 1994). A principal consequence of OSAS is
excessive daytime sleepiness (Guilleminault 1994). OSAS and snoring have potential
medical complications such as arterial hypertension, heart disease and brain infarction
(Partinen 1994).
Table 1. Diagnostic critria for the obstructive sleep apnea syndrome according to ICSD (1997)
__________________________________________________________________________________
A. The patient has a complaint of excessive sleepiness or insomnia. Occasionally, the patient may be
unaware of clinical features that are observed by others.
B. Frequent episodes of obstructed breathing during sleep.
C. Associated features include:
1.
2.
3.
4.
Loud snoring
Morning headaches
A dry mouth upon awakening
Chest retraction during sleep in young children.
D. Polysomnographic monitoring demonstrates:
1.
2.
More than five obstructive apneas, greater than 10 seconds in duration, per hour
of sleep and one or more of the following:
a. Frequent arousals from sleep associated with the apneas
b. Bradytachycardia
c. Arterial oxygen desaturation in association with the apneic episodes
MSLT may or may not demonstrate a mean sleep latency of less than 10
minutes.
E. The symptoms can be associated with other medical disorders (e.g. tonsillar enlargement).
F. Other sleep disorders can be present (e.g. periodic limb movement disorder or narcolepsy).
___________________________________________________________________________________________
Minimal criteria A + B + C. Reproduced with permission of the American Academy of Sleep Medicine.
___________________________________________________________________________________________
36
The MSLT findings in sleep apnea patients are variable and in clinical studies the patients
and controls do not necessarily differ from each other. In sleep apnea patients MSLT mean
latency is often, but not always, shortened (Guilleminault et al. 1988). On the other hand
short sleep latencies have been obtained in patients with low apnea indices and just
slightly disrupted sleep architecture (Valencia-Flores et al. 1993). As with other patient
groups or healthy subjects subjective sleepiness of apnea patients does not necessarily
predict the outcome of the MSLT (Dement et al. 1978). Attention has also been paid to
lack of differences in subjective sleepiness between OSAS patients and controls (Roth et
al. 1980).
Chervin and Aldrich (1998) used regression analysis to study the relationships between
MSLT and respiratory parameters in PSG in a population of 1046 patients. Several
parameters were significantly associated with sleepiness measured by MSLT. The supine
apnea-hypopnea index (AHI) predicted the MSLT results better than the total AHI. The
rate of obstructive apneas was also more relevant to excessive daytime sleepiness than the
rates of other types of respiratory events. In any case, in another study no relationships
between respiratory variables and the MSLT scores were found (Roth et al. 1980). In a
pooled data set with apnea patients and controls MSLT correlated moderately negatively
with nocturnal %S1 and shifts to S1. In the controls a positive relationship between MSLT
and the amount of wakefulness and a negative correlation between MSLT and shifts to S1
were obtained. No correlations between MSLT score and sleep parameters were found in
the patient group. It was concluded that daytime sleepiness is related to nocturnal sleep
disruption in normal subjects but not in patients.
2.7.5. Evaluation of MSLT as an indicator of sleepiness
It is generally accepted that the MSLT measures the tendency to sleep in a sleep-inducing
environment without disturbing factors (Carskadon 1986). Although subjective estimation,
performance tests and the MSLT scores give different results the MSLT is still regarded as
the most valid measure of sleepiness. MSLT is considered to be less influenced by
confounding factors such as muscle fatigue, motivation and practice than are performance
tests. In addition to evaluating sleepiness, it makes it possible to check for sleep onset
REM periods. (Richardson et al. 1978, Carskadon and Dement 1979).
In the opinion of Matousek and Petersén (1983) the poor definitions of subjective feelings
of vigilance makes subjective assessment of vigilance unreliable especially when different
individuals or different situations are compared. Reaction time studies are not satisfactory
because the measurements influence vigilance. These factors do not affect the EEG
recordings.
Subjective estimates of sleepiness may become unreliable if the subject has been adapted
to sleepiness for a very long time. In sleep apnea patients the progression of EDS may be
so slow that the patient becomes accustomed to it and no longer perceives sleepiness.
Sleepiness can also be denied if there is a fear of being labeled lazy or unmotivated
(Dement et al. 1978).
37
It has been stated that one night of poor sleep does not affect the MSLT result. The
shortening of MSLT latency would not be visible until after two nights of partial (5 h)
restriction (Carskadon and Dement 1981). On the other hand one night of sleep
fragmentation has been found to cause a change in the MSLT score without an impairment
of performance (Philip et al. 1994). It has been suggested that in sleep apnea patients the
MSLT is more sensitive and therefore gives different results than subjective estimation. It
has also been postulated that the MSLT is independent of motivational factors and the
need to deny sleepiness (Roth et al. 1980). Dement et al. (1978) also claimed that
performance testing can be confounded by motivational factors and therefore the MSLT
would be preferable.
However, it has been argued that motivational factors can also play a role in the MSLT.
Broughton (1994) has emphasised the role of compliance. In one study subjects who were
rewarded by economic incentives managed to stay awake longer than those who did not
receive any reward, even if subjective estimates of sleepiness and performance were the
same (Alexander et al. 1991). In another study, subjects fell asleep more quickly when
they were economically rewarded. The effect was, however, limited only to the afternoon
nap (Harrison et al. 1996).
Other than motivational factors may also influence the MSLT result. It has been shown
that even modest physical activity like 5 min. of walking prior to the nap as compared to
watching television can prolong the MSLT latency by approximately
6 min. (Bonnet
and Arand 1998). Walking also abolished the afternoon dip in MSLT latencies.
Sleepiness is not necessarily a single entity (Broughton 1982). MSLT can be effective in
differentiating between pathological and normal sleepiness but it may not be appropriate in
measuring subtle variations in sleepiness (Roth et al. 1980).
In summary, MSLT may measure the intensity of sleepiness but it is not useful in
evaluating the quality or the etiology of sleepiness. In clinical practice MSLT seems to be
useful in determining sleepiness related to narcolepsy. It can also be used in experimental
studies. Otherwise the MSLT is not very sensitive or specific. It is not clear whether the
reason is the test protocol itself or the way of scoring and the parameters used.
2.7.6. Increasing the sensitivity of MSLTs
Scoring with epochs is not necessarily a suitable method for analysing MSLTs. Due to the
fluctuating nature of waking and sleep at sleep onset it is somewhat accidental which
epoch is the first to be scored S1. Attempts have been made to increase the sensitivity of
the MSLT by using shorter epochs or detection of sleep episodes of shorter duration.
Determination of sleep latencies by scoring microsleep episodes has been applied by
several groups. Pressman and Fry (1989) defined sleep onset as the first 10 s of continuous
sleep because of sleep fragmentation due to apneas and hypopneas. Unfortunately they did
not score sleep onset also to the first S1-epoch for comparison. Therefore the effect of
microsleep scoring remains unclear. Harrison and Horne (1996b) obtained shorter MSLT
sleep latencies and more sleep onsets by using a 5 s continuous sleep episode than by using
38
conventional single-epoch sleep onset criteria. Valencia-Flores et al. (1995) compared 5,
10 and 30 s epochs in a 1-nap study. The percentage of subjects that could be classified as
severely sleepy was greater by the 5 s epoch criteria with respect to the standard 30 s epoch
duration.
Particularly in sleep apnea patients it is assumed that short microsleeps reveal loss of
vigilance more precisely than the conventionally scored MSLT mean latency. Obstructive
apneas and the related arousals are known to prevent or delay sleep onset in 4.8% to 28.6%
of MSLT naps when using a single epoch of S1 criteria (Browman and Winslow 1989).
Due to this many sleep laboratories have started to correct sleep onset latency of the MSLT
to the first respiratory event, even if it is not proposed in the MSLT guidelines. As far as is
known no additional studies about the effects of this method on patients and normals have
been conducted.
By adaptive segmentation the short vigilance changes could be detected (Hasan et al. 1993,
Himanen et al. 1999). Therefore adaptive scoring would be particularly suitable for
vigilance study and OSAS patients. Quantitative methods have also been applied to MSLT
demonstrating spectral differences within the sleep onset period of narcoleptics and normal
sleepers. (Alloway et al. 1999). Mean delta power was found to be higher in narcoleptic
naps containing REM sleep or S2 as compared to naps with S1 or S2 of control subjects.
As the homeostatic process can be exposed by slow wave activity, the use of quantitative
methods in MSLT diagnostics and in the study of sleep onset dynamics was suggested to
supplement visual judgement.
39
3. PURPOSE OF THE STUDY
The aims of the present study were:
1. To develop visual scoring of sleep recordings: to improve the temporal resolution of the
conventional scoring method. To reveal minor vigilance state fluctuations by defining new
vigilance stages.
2. To examine whether multiple sleep latency test parameters obtained by the new scoring
method would be better in differentiating between sleepy patients and healthy subjects
than the parameters derived from the conventional scoring method.
3. To find out whether the new scoring method would be suitable for the study of sleep
onset and sleep dynamics.
4. To study if the new scoring method would provide a better basis for automatic sleep
analysis than the conventional scoring method.
5. To examine whether the new vigilance stages defined purely by the morphology present
spectral differences specific to different vigilance states.
40
4. SUBJECTS AND METHODS
4.1. SUBJECTS
Most recordings were part of a larger project (Siesta). The patients were recruited through
newspaper advertisements or were referred by their physicians. The control subjects were
recruited through advertisements. Healthy volunteers had to be free from any sleep
complaints and they must not have had subjective EDS. At the beginning the author
interviewed all the patients and control subjects and performed a general physical
examination to exclude any primary medical or psychiatric disorder. None of the subjects
used hypnotics or other medication affecting the central nervous system.
The inclusion criteria in the study for the patients were a clinical picture and subjective
complaints of OSAS according to the International Classification of Sleep Disorders
(ICSD 1997, Table 1) and an AHI >10/h in the preceding polygraphic whole night
recording.
Exclusion criteria for control subjects were Mini-Mental State Examination score (MMSE,
Folstein et al. 1975) < 25, Pittsburgh Sleep Quality Index (PSQI, Buysse et al. 1989) > 5,
habitual bedtime before 22.00 or after 24.00, Zung Anxiety Scale (Self-rating anxiety
scale, SAS, Zung 1971) raw score > 33 and Zung Depression Scale (Self-rating depression
scale, SDS, Zung 1965) raw score > 35. Control subjects had to have an AHI of < 10/h.
Six men and four women of all the OSAS patients studied met the inclusion criteria. They
represented typical sleep apnea patients suffering from mild to severe disorder. Altogether
three men and four women were accepted as control subjects. All subjects gave their
written consent to participation in the study. Subjects were not paid for participating.
The median ages of the 10 patients and the 7 controls in the first part of the present study
were 54 years (range 35 - 63) and 49 years (25 - 77) respectively.
One female patient did not participate in psychological tests, vigilance test or reaction time
test and was not recruited for the psychometric part of study. In this part of the study the
median age for the patient group was 53 years (range 35 - 63).
All patients and control subjects with MSLTs without major technical failures were
included in the spectral part of the study. Since only one control subject presented low
voltage alpha activity his recordings were not included in the pooled data. This left six
patients and four control subjects out of total 17 subjects and one control subject with poor
alpha activity. Three patients and three control subjects were female. From one female
patient only three naps were studied by FFT due to technical difficulties. In this part of the
study the subjects ranged in age from 43 to 63. The control subject with poor alpha activity
was 25 years old.
41
4.2. RECORDINGS
The subjects underwent an investigation consisting of a whole night polygraphy together
with psychological tests, reaction time test, vigilance test and an MSLT the following day.
All studies were performed at the sleep laboratory of the Department of Clinical
Neurophysiology, Tampere University Hospital. The recordings were performed in a
sound-attenuated laboratory room in a controlled environment with a temperature of about
22 oC. The subjects attended to the laboratory at 7 p.m. and they retired to bed between 10
and 12 p.m. depending on their habitual bedtimes. They were allowed to sleep for a
maximum of 8 hours.
The MSLTs were started after psychometric tests, not until two hours from awakening.
The four naps were recorded at approximately 10:00, 12:00, 14:00 and 16:00. The
standard clinical guidelines for MSLT were followed (Carskadon 1986). The MSLT naps
were terminated 20 min. after lights out if there was no sleep at all. If the subject fell
asleep, the recording was continued for 15 minutes from the first sleep stage 1 (S1) epoch.
The subjects were instructed not to sleep between naps.
The daytime and night-time recording montages were similar. Seven EEG derivations Fp1M2, C3-M2, O1-M2, Fp2-M1, C4-M1, O2-M1, M2-M1, two EOG-channels EOG P8-M1,
EOG P18-M1 (Häkkinen et al. 1993) and submental muscle tonus were recorded. In
addition the following parameters were measured: m. tibialis anterior muscle tonus by
surface electrodes, body position, electrocardiogram, oro-nasal airflow by thermistor,
thoracoabdominal respiratory movements by piezo transducers and blood oxygen
saturation by oximetry.
A 16-channel Embla sleep recorder (Flaga) with a dynamic range of 16 bits and a
sampling rate of 200 Hz was utilised. This gave a bandwidth of approximately 90 Hz by
using a completely digital flat filter. The direct recording mode was used so that data was
sampled directly on the hard disk of a personal computer. This allowed on-line monitoring
of the signals in order to secure the quality of the recordings.
4.3. PSYCHOMETRIC TESTS
All subjects completed two psychological self-rated scale tests in advance. These were
Quality of Life Questionnaire (QOL) and Epworth Sleepiness Scale (Johns 1991) which
was slightly modified to suit the Finnish conditions better (mESS). All psychometric tests
were scored and supervised by undergraduate psychologists.
The psychometric tests were Adjective Mood Scale for subjective well-being (Bf-S, von
Zerssen et al. 1970), Alphabetical Cancellation Test, an alphabetic cross-out test for
attention and concentration (AD-test, Gruenberger 1977, pp. 148-151), Gruenberger FineMotor-Test for psychomotor activity (FM-test, Gruenberger 1977, pp. 155-162) and Digit
span test for numerical memory. These were carried out in the morning after getting
42
dressed and having breakfast (with habitual coffee and cigarettes, no alcohol) between 1
and 2 hours after getting up.
The “Vienna Reaction Time Test for Screen“ (Developed by Dr. G. Schuhfried) was used
to measure reaction time to visual and acoustic stimuli. Test duration was approximately 5
minutes. In this test two coloured circles were represented on the screen of the personal
computer. A concomitant sound stimulus was given with some of the visual stimuli. When
the left circle was yellow with the simultaneous acoustic stimulus the subject had to move
the finger from one button to another. The mean reaction time (RT-test) and number of
misses (RT-miss) were calculated for each subject.
A longer version of the Quatember-Maly vigilance test (Vigil, developed by Dr. G.
Schuhfried) was used to measure vigilance in a low-stimulus situation. Test duration was
45 minutes. In this test a bright dot moved along a circular path on a PC screen. If the dot
jumped twice the usual distance, the subject had to react by pressing the reaction button.
The mean value of reaction times was calculated (Vigil) for each subject. The difference
between the mean reaction times of the last and first thirds of the test session was also
calculated (VIGdiff).
4.4. VISUAL SCORING
4.4.1. Night recordings
The night recordings were scored into sleep stages by the standard method of
Rechtschaffen and Kales in epochs of 30 s (RKS, 1968). The following parameters were
measured. Time in bed (TIB) is the time from the beginning of the recording to the end of
the recording in minutes. Sleep onset latency (SOL) is the interval from lights off to the
first S1 epoch, which is followed by at least two consecutive sleep epochs or to the first
epoch of any other sleep stage, provided that it appears before the three consecutive S1
epochs. Total sleep time (TST) is the time spent asleep from sleep onset to the end of the
final sleep epoch. Sleep efficiency index (SEI) is the percentage of TST from TIB.
Percentages of total times of stages S1, S2, S3, S4 and SREM referred to TST were also
calculated.
Awakening is defined as at least one 30 s epoch containing more than 50% of
wakefulness. Awakening index (AwakeI) is the number of awakenings and epochs scored
as movement time (MT) per hour referred to TST. The Stage shift index (ShInd) is the
number of stage shifts per hour referred to TST.
Microarousals were scored according to guidelines suggested by the atlas task force of the
American Sleep Disorders Association (ASDA 1992). Arousal index (ASDARI) is the
number of these microarousals per hour. Microarousals were also scored according to the
arousal criteria of visual adaptive scoring of sleep (VASS) with the exception of minimum
duration of 2 s and a demand for 10 s of preceding sleep. The VASS criteria for arousals
are presented in Table 2. The definitions of the arousals
43
WL
WA
WAF
SA
SAF
DL
S1VASS
S2VASS
REMVASS
Wake-low
Wake-alpha
Wake-alpha-F
Alpha-SEM
Alpha-SEM-F
Drowsy-low
S1
S2
REM
Low-voltage mixed-frequency EEG with fast eye movements or no eye movements.
Posterior alpha in EEG with fast eye movements or no eye movements.
Diffuse or frontocentral alpha in EEG with fast eye movements or no eye movements.
Posterior alpha in EEG with SEMs.
Diffuse or frontocentral alpha in EEG with SEMs.
Low-voltage mixed-frequency EEG with SEMs.
EEG with increased theta activity.
Spindles and/or K complexes in EEG.
Theta activity, saw tooth waves in EEG with REMs and low muscle tonus.
Electrophysiological characteristics
Aa (1,2)
A-Ka (2)
A-delta (2)
A-desyn (1,2)
EMGW
EMGS
MTW
MTS
Appearance of alpha activity in EEG. From stages S1VASS and S2VASS, respectively.
K-complex with alpha activity in EEG. From S2VASS.
Abrupt hypersynchronous slow-waves in EEG with EMG augmentation. From S2VASS.
Phases with desynchronized EEG-patterns. From S1VASS and S2VASS, respectively.
Elevated muscle activity in wakefulness stages and in SA + SAF.
Elevated muscle activity in DL and in sleep stages.
Muscle activity with cable artifacts in wakefulness stages and in SA + SAF.
Muscle activity with cable artifacts in DL and in sleep stages.
SEM, slow eye movement; REM, rapid eye movement; EMG, submental muscle tonus.
Alpha-arousal (1, 2)
K-alpha-arousal (2)
Delta-arousal (2)
EEG attenuation (1, 2)
EMG-activity in wake
EMG-activity in sleep
Movement in wake
Movement in sleep
Arousals
In S1VASS and REMVASS additional EMG-increase or cable artifact is required.
Abbreviation
Stage
Table 2. Characteristics of stages in VASS
roughly follow the definitions of CAP phase A of Terzano et al. (1996) and “definite
arousals“ by Evans (1993). The index of these modified microarousals (mARI) is also
referred to TST. The Apnea-hypopnea index AHI is calculated as the hourly rate of
cessations or diminutions > 50% of airflow lasting over 10 s. ODI 4 is the number of
decreases in SaO2 of at least 4 %. SaO2 minimum (SaO2min) is the lowest value of
oxygen saturation in sleep.
4.4.2. MSLT scoring
All MSLTs were scored both by conventional sleep stage scoring (RKS) and by visual
adaptive scoring (VASS). RKS was based on the EEG derivation C4-M1. A 30 s
epoch was used as stated in the guidelines (Carskadon 1986).
In VASS the changes of the electrophysiological characteristics of the signal are used
to determine the segment boundaries instead of a fixed epoch (Figure 2).
Electrophysiological stage changes shorter than 1 s are not scored separately, which is
in line with the recommendation of the Comac BME task force (Kemp 1993). The
information from the frontal and occipital EEG derivations is also taken into account
by separating the occipital alpha activity from the more diffuse alpha activity. KCs,
which are sometimes identifiable only in frontal leads, are also used in scoring as well
as frontal spindles.
Figure 2. 27 s of polygraphic tracing. Channels from top to bottom: EEG Fp1-M2, C3-M2, O1-M2, Fp2-M1,
C4-M1, O1-M2 and M2-M1. EOGdx-M1, EOGsin-M1, submental EMG tonus.
RKS scoring is shown on trace 4, VASS on trace 5.
At the beginning there is low voltage activity with SEMs (Drowsy-low, DL), which is followed by S1VASS.
Somewhat later occipital alpha activity with SEMs (Alpha-SEM, SA) appears. This was not scored as an arousal
since no changes occurred in submental EMG-channel. S1VASS gives way to occipital alpha activity with eye
movements but not SEMs (Wake-alpha, WA). Next a short segment of Wake-low (WL) was scored (lowamplitude activity, no SEMs). At the end a short Alpha-SEM (SA) segment is followed by S1VASS.
45
The VASS stages with their descriptions are shown in Table 2. The stages were
chosen so that morphologically different states could be separated. VASS stages
Wake-low (WL), Wake-alpha (WA), Wake-alpha-frontalis (WAF), Alpha-SEM (SA)
and Alpha-SEM-frontalis (SAF) correspond to S0 in RKS (S0RKS). Drowsy-low
(DL) is defined by attenuation of alpha activity and presence of low-voltage EEG
activity combined with SEMs. Scoring of S1 in VASS (S1VASS) requires the
presence of theta activity. Both of these stages correspond to S1 in RKS (S1RKS).
The stages were separated because they are assumed to reflect different levels of
vigilance (Hasan et al. 1993). In some of the calculations DL and S1VASS were
combined in order to make RKS and VASS more comparable. S2VASS is identical to
S2RKS in this study, which concentrates on the evaluation of short daytime
recordings. The appearance of the first well formed spindle or KC is used as the
starting point of S2VASS. Arousals are scored as described above with a minimum
duration of 1 s. One arousal may consist of several different VASS stages (Figure 3).
When the number of arousals was calculated these several stages forming just one
arousal were combined. SEMs are defined as slow eye movements with a rise time >
0.5 s and a duration > 1 s.
Figure 3. 27 s of polygraphic recording, channels as in Figure 2.
At the beginning of the tracing the EEG has been scored as S2 by both methods. S2RKS is followed by
wakefulness (trace 4).
On trace 5 the VASS scoring demonstrates arousal with KC and alpha activity (arousal-K-alpha), which is
followed by EMG augmentation in sleep (EMGS) and alpha activity with SEMs (Alpha-SEM, SA).
46
The VASS stages are modified from the preliminary pilot VASS study (Himanen et al.
1999) where topographical differences of alpha activity could not be properly taken
into account. In the present study alpha activity with SEMs is also separated from the
alpha activity with other eye movements or blinks because alpha activity with SEMs is
specifically assumed to reflect impairment of vigilance (Kojima et al. 1981,
Santamaria and Chiappa 1987). Two new stages (WAF and SAF) were defined. To
examine in which stages the arousals occurred arousals are also coded by stage.
Scorings were done by using the Somnologica program version 1.6 (Flaga). For
VASS a special tool made by the aid of the developer’s toolkit provided by the
manufacturer was utilised. The PC computer used for analysis had a 21“ screen with a
1280 x 1024 resolution.
Repeatability of VASS
To examine the repeatability of the new scoring method the author re-scored 8 naps
blindly. The time interval between scorings was approximately one year. Five of the
naps were from apnea patients and three naps from control subjects. The percentage of
time with identical scorings was calculated for each nap separately (see Figure 4).
________________________________ __________________
__________
Figure 4. Comparison between two independent scorings. At the beginning of the tracing both scorings show
S2VASS. The starting point for Arousal-Ka2 differs slightly as does the ending time of the arousal. In the upper
scoring DL, which is followed by S1VASS, is shown, whereas in the lower scoring S1VASS is scored
immediately after the arousal. The black bars below mark the time when scorings are identical.
47
Definition of MSLT latencies
Latencies of the four naps in each MSLT were determined by RKS and VASS. Sleep
onset latency of RKS was calculated to the beginning of the first 30 s epoch of S1,
which was the first RKS sleep stage in each nap. Latencies to other RKS stages were
calculated to the first epoch of the corresponding stage. Latencies to VASS stages
were calculated from “lights off“ to the first appearance of the stage. If any sleep stage
was absent in a nap, a score of 20 min. was assigned to that stage. This corresponds to
the clinical practice of MSLT where a score of 20 min. is assigned to a nap if sleep
does not occur.
The mean sleep onset latencies of the four naps were calculated for each MSLT in
order to obtain the mean sleep onset latency of RKS (LatS1RKS), mean latency of
Drowsy low (LatDL) and mean latency of adaptively scored S1 sleep (LatS1VASS).
One subject who did not have DL was excluded from the statistical analyses when
LatDL was used as a variable. To study whether sleep onset latency adjustment to the
first apnea/hypopnea would be as sensitive as latencies obtained by VASS, the latency
to S1RKS was substituted with the latency to the first apnea/hypopnea if the latter was
shorter. This procedure gave an adjusted mean latency to first apneas/hypopneas
(LatHYP). The above-mentioned latencies and LatVASS - the shorter of LatDL or
LatS1VASS - were used to compare the sleep onset latencies of conventional scoring
to the new scoring system.
Mean latencies to S2 sleep were calculated correspondingly (LatS2RKS,
LatS2VASS). Latencies to the first appearance of SEMs in the naps were also
determined, and the mean latency to SEMs was defined for each MSLT (LatSEM).
In addition a few other mean latencies were calculated from VASS. The first
continuous sleep periods of 5 s, 10 s and 15 s were identified from each nap and the
mean sleep latencies to the beginning of 5 s (LatCon5), 10 s (LatCon10) and 15 s
(LatCon15) of continuous sleep were calculated. Mean sleep latencies to 5 s, 10 s, 15
s, 20 s, 30 s and 60 s of cumulative sleep were calculated (LatCum5, LatCum10,
LatCum15, LatCum20, LatCum30, LatCum60 respectively). In these calculations
sleep was defined as any combination of DL, S1VASS, S2VASS, REMVASS,
arousals, movements in sleep (MTS) or EMG-activity in sleep (EMGS).
4.5. SPECTRAL ANALYSIS
4.5.1. Calculation of EEG spectra
The mean frequency spectra of each VASS and RKS stage of each MSLT nap were
calculated by Fast Fourier Transform (FFT). A window of 256 data points was used.
With a sampling rate of 200 Hz the length of each segment analysed separately was
1.28 s. The average spectra and standard deviations of all 1.28 s units were then
calculated for each stage and nap. If the length of the segments was not divisible by
256 points zero filling was used. No window was used for the dampening of
oscillations. The procedure gave 128 spectral points divided between 0 and 100 Hz.
48
4.5.2. Comparison between RKS and VASS in the differentiation of wakefulness and
light sleep by alpha and delta-theta bands
In this work special attention was paid to studying spectral differences between
wakefulness and light sleep (S1) in RKS and VASS. Spectra from the EEG derivation
C4-M1 were used for comparisons, which were made between alpha (7.42-12.88) and
delta-theta (1.95-6.64) frequency bands. A small gap was left between the bands in
order to avoid overlapping. Median power of alpha and delta-theta bands were
calculated for RKS and VASS stages. The comparisons were done between S0RKS,
S1RKS, WA, WL and S1VASS. The reason for choosing the lower frequency range
of the delta-theta band was that it is the frequency range which is defined as theta
activity in sleep research although in EEG practice the frequencies between 2 to 4 Hz
are included in the delta band. These comparisons were also made separately for the
control subject with poor alpha activity.
Topographical differences of the alpha and delta-theta power bands in S0RKS,
S1RKS, WA, WL and S1VASS stages were studied by comparison between the
derivations Fp2-M1, C4-M1 and O2-M1.
4.5.3. Peak frequencies
Median peak frequencies and median amplitudes of the peaks of RKS and VASS
stages were calculated. In this analysis a wide frequency band (1.95-24.61 Hz) was
used. The use of a lower cut-off point would have given misleading results as the
power at 1.56 Hz was in general so high that it would have become the dominant
frequency. Comparison between the median peak frequencies of the RKS and VASS
stages was based on the EEG derivation C4-M1.
Topographical comparisons between the median peak frequencies of the following
VASS stages were also made: WA, SA, WAF, SAF, WL, DL, S1VASS and S2VASS.
4.5.4. Frequency band power characteristics of VASS stages
To study spectral characteristics of VASS stages the spectral power was divided into
five frequency bands: delta (0-3.51), theta (3.52-7.41), alpha (7.42-12.88), sigma
(12.89-16.01) and beta (16.02-24.60). Topographical power band comparisons were
performed within stages WA, SA, WAF, SAF, WL, DL, S1VASS, S2VASS, arousalalpha2 (Aa2), EMGW and MTW. Power band differences were also studied between
VASS stages.
49
4.6. STATISTICAL METHODS
The Matlab® version 5.2.0 was used for most calculations of the data and drawing of
hypnograms and spectra. The Statistica® for Windows program (release 5.1, StatSoft
Inc.) and SPSS® (release 9.0, SPSS Inc.) were used for statistical analyses. As the
variables were not normally distributed, non-parametric tests were utilised. The
Wilcoxon signed-rank test was used for comparison of paired differences, for example
for the comparisons between RKS and VASS. The Mann-Whitney U test was used for
comparison of variables between patient and control groups.
Sensitivity was calculated as the ratio of the number of patients with short MSLT
latencies divided by the number of patients. Specificity was calculated as the ratio of
the number of control subjects with long MSLT latencies divided by the number of
control subjects. One control subject who did not have DL at all was left out of
statistical analyses when latencies to DL were examined.
Spearman’s correlation coefficient was used in the psychometric part of the study to
examine the correlations between different variables. A correlation coefficient with an
absolute value > 0.5 was considered to indicate moderate correlation between
variables.
In binary logistic regression one of the latencies at a time was selected as an
independent variable. A cut-off point of 3 min. and 5 min. was used for latencies to
divide subjects into “sleepy“ and “normal“ groups. Variables derived from night
recordings and psychometric tests were used as dependent variables.
In the spectral part of the study the Friedman test was used in multiple comparisons
between stages and electrode sites. The Wilcoxon signed-rank test was used for posthoc analyses. In these post-hoc analyses the Bonferroni correction factor was used to
correct for multiple comparisons.
4.7. ETHICAL CONSIDERATIONS
The study was approved by the Ethical Committee of Tampere University Hospital.
50
5. RESULTS
5.1. SLEEP PARAMETERS OF NIGHT RECORDINGS
The sleep parameters derived from the night recordings are shown in Table 3. The
only parameters that differed significantly between the patient and control groups
were TST, AHI, ODI4 and SaO2min. Although patients had some more arousals by
both scoring methods, neither conventional arousal index (ASDARI) nor modified
arousal index (mARI) showed any significant differences between groups.
Table 3. Sleep parameters from night recordings preceding MSLTs
Patient group
Parameter
TIB (min)
TST (min)
SEI (%)
SOL (min)
%S1
%S2
%S3
%S4
%SREM
AwakeI /h
ShInd /h
ASDARI /h
mARI /h
AHI /h
ODI4 /h
SaO2min (%)
Median
490.0
446.5
91.1
14.5
6.5
62.7
9.0
1.0
20.4
2.9
18.6
15.4
22.2
20.0
25.5
77.0
Range
Control group
Median
475 - 498.5
422.5 - 473
86.7 - 95.9
0 - 26
4.1 - 15.6
40.7 - 78.6
4.1 - 15.3
0 - 12.9
10 - 25.8
2.2 - 4.6
13.4 - 22.0
8.9 - 68.2
9.5 - 68.1
10 - 80
9 - 71
41 - 88
484.5
407.0
83.2
23.0
6.9
55.5
9.2
9.0
18.7
2.3
16.2
13.4
15.0
3.0
3.0
89.0
Range
473 - 492
297.5 - 452
61.4 - 94.1
14 - 124.5
1.7 - 17.5
46.1 - 69.9
4.5 - 11.2
0 - 13.2
10.9 - 25.4
0.5 - 4.2
12.4 - 20.2
6.9 - 19.4
8.2 - 24.9
0-7
0-6
81 - 95
p-value
0.12
0.040
0.12
0.079
0.77
0.12
0.92
0.11
0.70
0.20
0.14
0.63
0.14
< 0.001
< 0.001
0.008
Percentages of sleep stages are referred to total sleep time.
TIB, time in bed; TST, total sleep time; SEI, sleep efficiency index; SOL, sleep onset latency; AwakeI,
awakening index, awakenings > 30s per hour; ShInd, stage shift index per hour; ASDARI,
ASDA-arousal index; mARI, modified arousal index; AHI, apnea-hypopnea index; ODI4, oxygen
desaturation index; SaO2min, lowest oxygen saturation percent.
Comparison between groups by the Mann-Whitney U-test.
51
5.2. SLEEP IN MSLT NAPS: COMPARISON BETWEEN PATIENT AND
CONTROL GROUPS
5.2.1. General characteristics
In the MSLT naps the shortest segment scored by VASS was 1 s and the longest
segment was 12 min. 35 s.
Patients had sleep by RKS in 37 out of 40 naps; by VASS sleep was found in every
nap. The results are presented as percentages in Figure 5. S2RKS occurred in 28 naps
and S2VASS in 31 naps. Respiratory events occurred in 26 naps. In the control group
sleep was scored by RKS in 16 out of 28 naps. By VASS sleep existed in 25 naps.
S2RKS and S2VASS were found in 13 naps. Respiratory events took place in 7 naps.
100
90
80
70
60
Patients
50
Controls
40
30
20
10
0
RKS
VASS
S2RKS
S2VASS
Resp.events
Figure 5. Percentages of naps with RKS sleep, VASS sleep, S2RKS, S2VASS and respiratory events. Comparison
between patients and controls. By VASS more naps with sleep are also obtained in the control group.
5.2.2. Sleep parameters in MSLT recordings
Sleep parameters derived from MSLTs with comparison between the patient and
control groups are presented in Table 4. The control subjects had slightly but
significantly longer MSLT sessions than the patients (p = 0.045). This is explained by
the longer extensions of the naps caused by later sleep onsets. As expected, control
subjects had a higher percentage of wakefulness than the patients analysed both by
RKS (%S0RKS) and VASS (%S0VASS) (p = 0.032 and p = 0.019 respectively).
Patients had more S1 than control subjects by both scoring methods (p = 0.006 for
%S1RKS and 0.002 for %VASS stages). In these calculations S1VASS and DL were
combined in order to make RKS and VASS more comparable. The differences
52
between groups in the amount of S2 were not statistically significant (p = 0.44 for
%S2RKS and 0.50 for %S2VASS). The reason could be the artificial termination of
the naps after 15 min. of sleep. The differences between groups in percentages of time
occupied by VASS arousals did not reach statistical significance, either (p = 0.097).
Only one patient had a short episode of REM sleep in one nap. The stage shift indexes
in RKS (ShiRKS) calculated from sleep period time (SPT) did not differ between
groups (patients 0.7/min., control group 0.6/min.). In VASS the median stage shift
index (ShiVASS) was significantly higher in the patient group than in the control
group (3.2/min. and 2.3/min., respectively).
Table 4. MSLT parameter comparison between patient and control groups
Patient group
Parameter
Median
TIB (h:min:s)
%S0RKS
%S0VASS
%S1RKS
%DL+S1VASS
%S2RKS
%S2VASS
%arousalsVASS
%REMRKS
%REMVASS
ShiRKS /min
ShiVASS /min
1:24:15
59.3
56.4
21.7
24.3
14.4
15.6
1.5
0.0
0.0
0.7
3.2
Control group
Range
Median
1:20:00 - 1:34:00
26.7 - 92.5
24.5 - 88.4
6.3 - 27.3
10.5 - 44.5
1.3 - 50.6
1.0 - 44.0
0.0 - 9.1
0.0 - 1.2
0.0 - 0.1
0.3 - 1.0
2.1 - 4.3
Range
p-value
1:31:00
78.8
76.8
9.9
10.3
10.5
9.7
0.7
1:24:30 - 1:45:00
65.2 - 89.9
62.7 - 88.5
7.7 - 13.4
7.6 - 16.0
2.4 - 26.7
2.2 - 25.6
0.0 - 1.5
0.045
0.032
0.019
0.006
0.002
0.44
0.50
0.097
0.6
2.3
0.5 - 0.8
1.6- 4.2
0.20
0.025
Percentages of sleep stages are referred to total sleep time of the four naps.
TIB, time in bed, total duration of the four naps; ShiRKS, stageshift index in RKS, referred to sleep
period time; ShiVASS, stageshift index in VASS.
Comparison between groups by the Mann-Whitney U-test.
5.2.3. MSLT latencies
As expected, sleep onset latencies (LatS1RKS, LatDL and LatS1VASS) were
significantly shorter in the patient group than in the control group (p = 0.001, 0.002,
0.003 respectively. Table 5). The mean latencies to S2 (LatS2RKS, LatS2VASS) were
also shorter in the patient group (p = 0.028 and 0.015 respectively). The mean latency
to the first appearance of SEM (LatSEM) was short in both groups but it was
significantly shorter in the patient group than in the control group (p = 0.025).
Interestingly, the time period from LatSEM to either LatS1RKS or LatS1VASS was
significantly longer in the control group than in the patient group (p = 0.015 and 0.011
respectively).
53
Table 5. MSLT latency comparison between patient and control groups
Patient group
Latency
LatS1RKS (min:s)
LatDL (min:s)
LatS1VASS (min:s)
LatS2RKS (min:s)
LatS2VASS (min:s)
LatVASS (min:s)
LatSEM (min:s)
LatS1RKS - LatSEM
LatS1VASS - LatSEM
LatCon5 (min:s)
LatCon10 (min:s)
LatCon15 (min:s)
LatCum5
LatCum10
LatCum15
LatCum20
LatCum30
LatCum60
LatHYP (min:s)
Median
7:26
2:13
4:21
13:52
13:15
2:12
0:47
4:32
2:43
3:31
5:01
6:03
2:27
3:20
4:00
4:42
5:10
7:09
5:43
Control group
Range
2:15 - 12:37
1:21 - 7:07
1:23 - 13:27
7:38 - 19:45
6:55 - 19:47
1:21 - 7:07
0:13 - 5:05
1:54 - 11:26
0:41 - 12:17
1:19 - 10:54
1:44 - 11:42
3:47 - 14:48
1:24 - 8:58
1:35 - 11:00
2:00 - 11:06
2:20 - 11:40
2:33 - 12:14
3:58 - 14:53
1:41 - 11:52
Median
17:07
12:43
13:10
19:00
19:19
11:16
3:11
14:16
10:23
13:51
15:39
16:03
13:19
14:35
14:55
15:18
16:10
17:33
15:15
Range
p-value
11:00 - 18:37
4:44 - 20:00
9:58 - 15:54
13:30 - 19:45
13:19 - 19:49
4:44 - 13:35
0:53 - 10:16
6:25 - 17:44
5:24 - 13:58
5:36 - 14:42
10:30 - 16:01
12:05 - 18:21
5:13 - 14:24
8:48 - 14:52
9:04 - 15:11
10:28 - 15:45
11:42 - 16:46
14:47 - 19:09
11:00 - 18:00
0.001
0.002
0.003
0.028
0.015
0.002
0.025
0.015
0.011
0.002
< 0.001
0.002
0.002
0.001
0.001
0.001
0.001
0.002
< 0.001
LatS1RKS, the mean sleep latency by RKS; LatDL, the mean sleep latency to DL;
LatS1VASS, the mean sleep latency to S1VASS; LatS2RKS, the mean latency to S2RKS;
LatS2VASS, the mean latency to S2VASS; LatVASS, the shorter of LatDL or LatS1VASS;
LatSEM, the mean latency to the first appearance of SEMs; LatS1RKS - LatSEM, time period
between first SEM and S1RKS; LatS1VASS - LatSEM, time period between first SEM and S1VASS;
LatCon5-15; the mean latency to the beginning of the first 5s, 10, and 15 s of continuous sleep,
respectively; LatCum5-60, mean sleep latency to cumulative sleep amount; LatHYP, the mean sleep
latency to the shorter of S1RKS or the first apnea/hypopnea.
Comparison between groups by the Mann-Whitney U-test.
Patients had 5 s of continuous sleep in 39 out of 40 naps (Figure 6 a). 10 s of
continuous sleep occurred in 38 naps and 15 s of continuous sleep in 36 naps. Control
subjects had 5 s of continuous sleep in 24 out of 28 naps. 10 s of continuous sleep
occurred in 19 naps and 15 s in 18 naps (Figure 6 b). The differences between groups
were significant for 10 and 15 s of continuous sleep patients having more naps with
10 and 15 s of sleep (p = 0.001 and 0.008, respectively). The mean latencies of
continuous sleep (LatCon5, LatCon10 and LatCon15, Table 5) were all significantly
shorter in the patient group.
Mean MSLT latencies from onset of the recording until accumulation of 5s, 10s, 15s,
20s, 30s and 60s sleep (LatCum5-60, respectively) were significantly shorter in the
patient group (Table 5). Patients had 5 - 15 s of cumulative sleep in all naps, 20 – 30 s
of cumulative sleep in 38 naps out of 40 and 60 s of cumulative sleep in 37 naps
(Figure 6 a). Controls had 5 s of cumulative sleep in 25 out of 28 naps, 10 s of
cumulative sleep in 24 naps, 15 – 20 s of cumulative sleep in 22 naps, 30 s of
cumulative sleep in 20 naps and 60 s of cumulative sleep in 19 naps (Figure 6 b).
54
Patients
40
36
32
Num ber of naps
28
24
20
16
12
8
4
0
RKS
VASS
Con 5
Con 10
Con 15
Cum 5
Cum 10
Cum 15
Cum 20
Cum 30
Cum 60
Cum 10
Cum 15
Cum 20
Cum 30
Cum 60
Controls
28
24
Num ber of naps
20
16
12
8
4
0
RKS
VASS
Con 5
Con 10
Con 15
Cum 5
Figures 6 a and b. Number of naps with any stage of sleep scored by RKS and VASS in the patient and control
group.
In one nap of the patients RKS sleep was scored but 15 s of continuous sleep was not reached by VASS. Three
naps in the control group contain 10 s of continuous sleep and two naps 15 s of continuous sleep even if sleep
could not be scored by RKS.
In three naps of the controls even 60 s of cumulative sleep was reached, but RKS sleep could not be scored.
Note the different number of total naps between groups.
Con 5, 10 and 15 = number of naps with 5, 10 and 15 s of continuous sleep, respectively. Cum 5, 10, 15, 20, 30
and 60 = number of naps with 5, 10, 15, 20, 30 and 60 s of cumulative sleep respectively.
55
In the patient group apneas/hypopneas appeared before S1RKS in 11 naps and in the
control group in 4 naps. To study whether correction of sleep latency to the first
apnea/hypopnea would give more specific information about sleepiness, the latency to
S1RKS was substituted with the latency to the first apnea/hypopnea if the latter was
shorter. This gave an adjusted mean latency (LatHYP). LatHYP was significantly
shorter in the patient group.
5.3. CHARACTERISTICS OF VASS PARAMETERS WITH COMPARISON TO
RKS
5.3.1. MSLT parameters
In the patient group less S2 was obtained by VASS than by RKS (Figure 7). This was
the only statistically significant difference between the percentage of stages when the
groups were studied separately (Table 6 and Table 7). VASS revealed more stage
shifts than RKS in both groups.
60
50
40
30
%S2RKS
20
10
0
0
10
20
30
40
50
60
%S2VASS
Figure 7. VASS resulted in a decreased amount of S2 during MSLTs in the patient group.
56
Table 6. MSLT parameter comparison between RKS and VASS in the patient group
Parameter
%S0RKS
%S0VASS
%S0RKS - %S0VASS
%S1RKS
%DL+S1VASS
%S1RKS - %S1VASS+DL
%S2RKS
%S2VASS
%S2RKS - %S2VASS
ShiRKS /min
ShiVASS /min
ShiRKS - ShiVASS
Median
Range
59.3
56.4
1.7
21.7
24.3
-3.4
14.4
15.6
1.3
0.7
3.2
-2.4
26.7 - 92.5
24.5 - 88.4
-3.6 - 8.0
6.3 - 27.7
10.5 - 44.5
-21.5 - 4.0
1.3 - 50.6
1.0 - 44.0
-4.3 - 27.8
0.3 - 1.0
2.1 - 4.3
-3.6 - -1.2
p-value
0.28
0.093
0.037
0.005
Percentages of sleep stages are referred to total sleep time of the four naps.
ShiRKS, stageshift index in RKS, referred to sleep period time; ShiVASS, stageshift index in VASS.
Wilcoxon test for paired differences.
Table 7. MSLT parameter comparison between RKS and VASS in the control group
Parameter
%S0RKS
%S0VASS
%S0RKS - %S0VASS
%S1RKS
%DL+S1VASS
%S1RKS - %S1VASS+DL
%S2RKS
%S2VASS
%S2RKS - %S2VASS
ShiRKS /min
ShiVASS /min
ShiRKS - ShiVASS
Median
Range
78.8
76.8
1.5
9.9
10.3
-2.3
10.5
9.7
1.0
0.6
2.3
-1.6
65.2 - 89.9
62.7 - 88.5
-2.3 - 5.0
7.7 - 13.4
7.6 - 16.0
-2.9 - 1.3
2.4 - 26.7
2.2 - 25.6
-1.5 - 3.8
0.5 - 0.8
1.6 - 4.2
-3.5 - -1.1
p-value
0.24
0.063
0.13
0.018
Percentages of sleep stages are referred to total sleep time of the four naps.
ShiRKS, stageshift index in RKS, referred to sleep period time; ShiVASS, stageshift index in VASS
Wilcoxon test for paired differences.
57
5.3.2. Latencies
In the patient group most latency comparisons between RKS and VASS reached
statistical significance. In general VASS latencies were shorter than RKS latencies
(Table 8, Figure 8). Both LatDL and LatS1VASS were significantly shorter than
LatS1RKS. LatS2VASS was also shorter than LatS2RKS. LatSEM was significantly
shorter than LatDL, LatS1VASS or LatS1RKS.
LatHYP was shorter than LatS1RKS but longer than LatDL or LatVASS (the shorter
of LatDL and LatS1VASS). The difference between LatHYP and LatS1VASS was
not significant. LatVASS was significantly shorter than LatCon5. LatCon5 was
shorter than LatS1RKS.
Table 8. MSLT latency comparisons in the patient group
Parameter
LatS1RKS
LatDL
LatS1RKS - LatDL
LatS1VASS
LatS1RKS - LatS1VASS
LatS2RKS
LatS2VASS
LatS2RKS - LatS2VASS
LatSEM
LatSEM - LatS1RKS
LatSEM - LatDL
LatSEM - LatS1VASS
LatHYP
LatHYP - LatS1RKS
LatHYP - LatDL
LatHYP - LatS1VASS
LatHYP - LatVASS
LatCon5
LatCon10
LatCon15
LatVASS
LatCon5-LatVASS
LatS1RKS - LatCon5
LatHYP - LatCon5
LatCum5
LatCum10
LatCum15
LatCum20
LatCum30
LatCum60
LatS1RKSnap - LatCon10nap
Median
(min:s)
Range
(min:s)
7:26
2:13
4:01
4:21
1:37
13:52
13:15
0:18
0:47
-4:32
-1:27
-2:43
5:43
-0:23
2:49
1:13
2:49
3:31
5:01
6:03
2:12
1:07
1:12
0:40
2:27
3:20
4:00
4:42
5:10
7:09
0:20
2:15 - 12:37
1:21 - 7:07
0:41 - 7:36
1:23 - 13:27
-0:50 - 5:22
7:38 - 19:45
6:55 - 19:47
-0:02 - 1:22
0:13 - 5:05
-11:26 - -1:54
-5:56 - -0:26
-12:17 - -0:41
1:41 - 11:52
-4:28 - 0:00
-0:15 - 5:11
-0:27 - 2:09
0:18 - 5:11
1:19 - 10:54
1:44 - 11:42
3:47 - 14:48
1:21 - 7:07
0:00 - 6:10
0:07 - 5:37
-3:02 - 3:55
1:24 - 8:58
1:35 - 11:00
2:00 - 11:06
2:20 - 11:40
2:33 - 12:14
3:58 - 14:53
-5:35 - 7:25
p-value
0.005
0.017
0.009
0.005
0.005
0.005
0.028
0.007
0.11
0.005
0.007
0.005
0.28
0.13
Wilcoxon test for paired differences. For abbreviations see Table 5.
LatS1RKSnap, latency to the first 30-s epoch of S1 calculated for each nap; LatCon10nap, latency to
10 s of continuous sleep calculated for each nap.
58
In the control group, also in general, all VASS latencies were shorter than RKS
latencies (Table 9, Figure 8). An exception was the difference between LatS2RKS
and LatS2VASS, which was actually close to zero and not significant. LatSEM was
shorter than LatS1RKS, LatDL and LatS1VASS. The differences between LatHYP
and LatS1RKS, LatDL or LatS1VASS were not significant, but LatHYP was longer
than LatVASS. LatCon5 was significantly longer than LatVASS. LatS1RKS was
significantly longer than LatCon5. LatHYP was significantly longer than LatCon5.
LatCum5-20 were significantly shorter than LatS1RKS. As controls reached 5 s of
continuous sleep in only 24 naps out of 28 naps and 10 s of continuous sleep in 19 out
of 28 naps, reliable comparisons between LatCon10 and other latencies could not be
made. The same applies to cumulative latencies.
Table 9. MSLT latencies and comparisons in the control group
Parameter
LatS1RKS
LatDL
LatS1RKS - LatDL
LatS1VASS
LatS1RKS - LatS1VASS
LatS2RKS
LatS2VASS
LatS2RKS - LatS2VASS
LatSEM
LatSEM - LatS1RKS
LatSEM - LatDL
LatSEM - LatS1VASS
LatHYP
LatHYP - LatS1RKS
LatHYP - LatDL
LatHYP - LatS1VASS
LatHYP - LatVASS
LatCon5
LatCon10
LatCon15
LatVASS
LatCon5-LatVASS
LatS1RKS - LatCon5
LatHYP - LatCon5
LatCum5
LatCum10
LatCum15
LatCum20
LatCum30
LatCum60
LatS1RKSnap - LatCon10nap
Median
(min:s)
Range
(min:s)
17:07
12:43
5:20
13:10
2:05
19:00
19:19
0:04
3:11
-14:16
-11:44
-10:23
15:15
0:00
2:32
1:41
4:13
13:51
15:44
16:30
11:16
1:41
2:18
2:32
13:19
14:35
14:55
15:18
16:10
17:33
0:50
11:00 - 18:37
4:44 - 20:00
1:29 - 6:51
9:58 - 15:54
1:02 - 7:21
13:30 - 19:45
13:19 - 19:49
-0:49 - 0:14
0:53 - 10:16
-17:44 - -6:25
-13:20 - 0:00
-13:58 - -5:24
11:00 - 18:00
-3:37 - 0:00
-5:37 - 6:16
-2:18 - 4:25
1:41 - 6:16
5:36 - 14:42
10:35 - 16:11
12:15 - 18:31
4:44 - 13:35
0:52 - 2:41
1:24 - 5:40
-0:17 - 5:24
5:13 - 14:24
8:48 - 14:52
9:04 - 15:11
10:28 - 15:45
11:42 - 16:46
14:47 - 19:09
-3:14 - 5:00
p-value
0.028
0.018
1.00
0.018
0.018
0.018
0.068
0.18
0.13
0.018
0.018
0.018
0.028
0.041
Wilcoxon test for paired differences. For abbreviations see Table 5.
LatS1RKSnap, latency to the first 30-s epoch of S1 calculated for each nap; LatCon10nap, latency to
10 s of continuous sleep calculated for each nap.
59
Patients
minutes
Controls
0
LatSEM
LatCum5
LatCum10
LatCum15
LatCum20
LatCum30
LatVASS, LatDL
LatSEM
LatCon5
LatS1VASS
LatCon10
5
LatHYP
LatCon15
LatCum60
LatS1RKS
10
LatVASS
LatS2VASS
LatCum5
LatS2RKS
LatDL
LatS1VASS
LatCon5
15
LatCum10
LatCum15
LatCum20
LatCum30
LatHYP
LatCon10
LatCon15
LatS1RKS
LatCum60
LatS2RKS
LatS2VASS
20
Figure 8. Representation of mean latencies in the patient and control groups. All latencies are shorter in the patient
group. The interval between LatSEM and LatVASS is less than 2 min. in the patient group. Although LatSEM is
only about 3 min. in the control group, the mean sleep onset latency, even by VASS, is longer than 11 min.
60
The relationships between latencies were somewhat different in the two groups.
LatS2VASS was significantly shorter than LatS2RKS only in the patient group. As
expected, in the patient group LatHYP was significantly shorter than LatS1RKS,
whereas this was not the case in the control group. In the patient group there was 5 s
of continuous sleep before LatS1RKS whereas the controls reached 10 s of continuous
sleep before LatS1RKS. As MSLT is based on mean values, where a missing
parameter is given the value of 20 minutes, the latencies cannot as such be used in the
study of sleep dynamics. This result was therefore verified by comparing the latencies
obtained from naps instead of the mean latencies. Both groups were analysed
separately. Controls reached 10 s of continuous sleep significantly earlier than sleep
could be scored by RKS (LatS1RKSnap - LatCon10nap, Table 9). In the patient group
the difference between LatS1RKSnap and LatCon10nap did not reach statistical
significance (Table 8).
61
5.4. CLINICAL EVALUATION OF MSLT
In clinical evaluation short MSLT latencies < 5 min. are considered pathological
indicating EDS (Richardson et al. 1978, Roehrs and Roth 1992). Medium latencies
between 5 - 10 min. are borderline and long latencies > 10 min. normal. In the present
study three of the 10 patients had long latencies, another three had medium latencies
and four patients had short latencies by RKS (Table 10). By LatDL the latency of one
patient changed from long to short, latencies of two patients changed from long to
medium and two latencies changed from medium to short. By LatS1VASS the latency
of one patient changed from long to short, the latency of another patient changed from
long to medium and two latencies changed from medium to short. By LatS1RKS all
control subjects had long latencies. By LatDL only one got a short latency. By
LatS1VASS the same subject got a medium latency.
This gave a sensitivity of 40% and specificity of 100 % to LatS1RKS in revealing the
OSAS related EDS if the 5 min. criterion is applied. The corresponding sensitivity and
specificity of LatS1VASS were 70 % and 100 %. LatDL gave a sensitivity of 70 %
with a specificity of 86 %. The subject in the control group who got short latencies by
VASS had an apnea/hypopnea index > 5. If latencies adjusted to apneas/hypopneas
(LatHYP) were used only two patients with long latencies got medium latencies and
no changes occurred in the control group. Sensitivity remained 40% with the
specificity of 100%.
Table 10. Clinical evaluation of MSLT by VASS and RKS
Group
ID
LatS1RKS
LatDL
LatS1VASS
LatHYP
Patients
1
2
3
4
5
6
7
8
9
10
L
S
L
S
S
L
M
M
M
S
M
S
M
S
S
S
S
S
M
S
L
S
M
S
S
S
S
S
M
S
M
S
L
S
S
M
M
M
M
S
Controls
11
12
13
14
15
16
17
L
L
L
L
L
L
L
L
L
L
L
S
L
L
L
L
L
L
M
L
L
L
L
L
L
L
L
L
S, short MSLT mean latency (< 5 min.); M, medium latency (5-10 min.); L, long latency (> 10 min.).
62
5.5. EFFECTS OF VASS ON SLEEP ANALYSIS
5.5.1. Effects of VASS on epoch lengths
The distributions of the duration of the scored segments obtained by both scoring
methods are shown in Figures 9 a - c. The distributions of WL, WA, WAF, SA and
SAF appeared similar by visual judgement and were therefore combined to form one
VASS wakefulness state. Likewise the distributions of S1VASS and DL were
combined. The distributions of the patient and control groups were almost similar in
appearance. Because of the small differences between the groups pooled data are
shown. Consecutive RKS epochs with the same stage were united to form a single
segment.
Of the segments in VASS wakefulness stages 96 % lasted < 30 s and 89 % of
segments were < 15 s (Figure 9 a). Ninety-three percent of S1VASS+DL segments
were < 30 s and 79 % < 15 s (Figure 9 b). As the majority of VASS scored
wakefulness and S1VASS+DL segments were shorter than 15 s more than one third of
S0RKS episodes were of 30 s duration and one fourth of the episodes were longer
than five minutes (Figure 9 a). Over half of the S1RKS episodes consisted of only one
epoch. Long episodes were rare (Figure 9 b). By VASS both short and long S2
episodes were obtained (Figure 9 c). Altogether 55 % of VASS scored S2 episodes
were < 30 s and 37 % < 15 s. The durations of S2RKS epochs were almost evenly
distributed with a slight dominance of 30 s segments.
55
50
45
40
35
30
25
S0RKS
Wake VASS
20
15
10
5
0
Segment length (s)
Figure 9 a. Proportional distributions of the duration of wakefulness segments obtained by RKS and VASS. The
majority of VASS segments are short, 89 % of segments being < 15 s. These would remain undetected by standard
scoring. One third of S0RKS episodes are 30 s long and one fourth of the episodes are longer than five minutes. By
RKS long wakefulness segments are obtained even if VASS shows no long segments.
63
55
50
45
40
35
30
S1RKS
S1VASS+DL
25
20
15
10
5
0
Segment length (s)
Figure 9 b. Most S1VASS+DL segments are short (79 % < 15 s) and would therefore be undetectable by RKS.
RKS overestimates the duration of continuous S1 sleep episodes.
25
20
15
S2RKS
S2VASS
10
5
0
Segment length (s)
Figure 9 c. By VASS both short and long S2 episodes are obtained. RKS is not capable of visualising S2 episodes
fragmented by arousals.
64
5.5.2. Stage transitions in VASS
The proportions of VASS stage transitions are shown in Tables 11 a and b. The
controls had on average 716 VASS stage transitions/subject/MSLT whereas the
patients had a mean number of VASS stage transitions of 672. However, in the
control group 83.3 % of the transitions occurred between wakefulness stages (WA,
WAF, SA, SAF, WL and movements + EMG augmentation in wakefulness = EMTW)
whereas the corresponding percentage for the patient group was 62.5 %. In the
controls 6.6 % of stage transitions were from wakefulness to sleep and 6.3 % sleep to
wake. The corresponding percentages for the patients were 13.7 % and 13.2 %. The
percentages of stage changes within sleep were 3.7 % for the control group and 10.5
% for the patient group.
65
SA
2.2
0
2.2
0.3
0.8
2.8
2.8
0
0
0
0
0.1
0
11.1
WL
17.8
1.8
0
2.0
0.4
1.3
1.1
0
0
0
0
1.5
0
25.8
WAF
2.1
0.3
1.9
0
0.3
0.1
0.6
0
0
0
0
0.1
0
5.5
SAF
0.5
0.6
0.5
0.1
0
0.9
0.9
0
0
0
0
0.0
0
3.5
SA
4.6
0
0.8
1.4
1.2
0.5
0.5
0
0
0
0
0.0
0
9.0
WL
11.4
0.8
0
4.0
0.6
0.9
0.8
0
0
0
0
3.7
0
22.3
WAF
8.1
1.2
4.7
0
1.1
0.0
0.8
0
0
0
0
2.4
0
18.3
SAF
0.8
0.7
0.8
0.8
0
0.6
0.9
0
0
0
0
0.1
0
4.7
DL
0.4
0.4
0.8
0.2
0.4
0
0.5
0
0
0
0
0
0.0
2.7
DL
1.1
2.2
1.5
0.2
0.7
0
1.9
0.0
0
0
0
0
0.0
7.6
S2VASS
0
0
0
0
0
0
0
0
0
0.0
2.1
0
0.2
2.2
S1VASS
1.0
0.3
0.7
0.9
0.6
0.4
0
0.9
0
0.1
0
0.1
0.3
5.3
S2VASS
0.0
0
0
0
0
0
0
0
0
0
0.7
0
0.1
0.8
This VASS stage….
S1VASS
2.3
1.7
1.7
0.5
0.4
1.2
0
2.4
0.0
0.4
0
0
0.1
10.8
This VASS stage….
REMVASS
0
0
0
0
0
0
0
0
0
0
0
0
0
0.0
REMVASS
0
0
0
0
0
0
0
0.0
0
0
0
0
0
0.0
Arousal-1
0
0
0
0
0
0.0
0.1
0
0
0
0
0
0.0
0.1
Arousal-1
0.0
0.1
0.1
0
0
0.0
0.2
0
0
0
0
0
0.0
0.4
Arousal-2
0.0
0.0
0.2
0.0
0.0
0
0.3
0.0
0
0
0
0.0
0.1
0.7
Arousal-2
0.0
0.0
0.3
0.0
0.0
0.3
1.1
0.1
0
0
0
0.0
0.3
2.2
EMTW
1.4
0.1
3.4
2.7
0.1
0
0.1
0
0
0
0
0
0
7.7
EMTW
1.1
0.1
1.5
0.1
0.0
0
0.0
0
0
0
0
0
0
2.8
EMTS
0.0
0
0.1
0.1
0
0.0
0.1
0.0
0
0.0
0.1
0
0
0.5
EMTS
0.1
0.1
0.0
0
0.0
0
0.1
0.0
0
0.0
0.1
0
0
0.6
Total
27.8
9.1
22.1
18.3
4.7
2.7
5.3
0.9
0.0
0.1
0.7
7.7
0.5
100.0
Total
27.3
11.1
25.7
5.5
3.5
7.6
10.9
2.5
0.0
0.4
2.2
2.7
0.6
100.0
For most abbreviations see Table 2. Arousal-1, all arousals from S1VASS; Arousal-2, all arousals from S2VASS; EMTW, movements and EMG augmentation in wakefulness; EMTS, movements and EMG
augmentation in sleep.
…is followed by this stage.
WA
WA
0
SA
5.5
WL
10.6
WAF
8.3
SAF
0.8
DL
0.3
S1VASS
1.1
S2VASS
0
REMVASS
0
Arousal-1
0
Arousal-2
0
EMTW
1.3
EMTS
0
Total
27.9
Table 11 b: Proportions of VASS stage transitions in the control group
…is followed by this stage.
WA
WA
0
SA
4.2
WL
16.0
WAF
2.3
SAF
0.9
DL
1.2
S1VASS
2.0
S2VASS
0
REMVASS
0
Arousal-1
0
Arousal-2
0
EMTW
1.0
EMTS
0
Total
27.5
Table 11 a: Proportions of VASS stage transitions in the patient group
5.5.3. Effects of VASS on hypnograms
The effects of VASS are clearly visible in the hypnograms of Figures 10 a - c with
MSLTs of three subjects. The patient in Figure 10 a has a borderline LatS1RKS
(8 min. 22 s). By VASS the repetitive short drop offs and awakenings starting soon
after the onsets of the recordings are visualised. LatVASS (the shorter of LatDL or
LatS1VASS) is 2 min. 38 s. The patient in Figure 10 b has short LatS1RKS and
LatVASS (2 min. 15 s and 1 min. 21 s respectively). By RKS it appears that he is
mostly sound asleep until the end of the naps. By VASS the repetitive arousals related
to the apneas fragmenting sleep are revealed. The differences between RKS and
VASS are not so marked in the hypnograms of the control subject in Figure 10 c.
However, short drop offs can be seen before sleep onset can be scored by RKS. More
awakenings can also be seen by VASS than by RKS. The latencies are normal both by
RKS (16 min. 38 s) and by VASS (11 min. 44 s).
Pat 8, MSLT 1
Pat 8, MSLT 2
Arousal
Arousal
Wake
SA+SAF
Wake
SA+SAF
REMVASS
REMVASS
DL
S1VASS
DL
S1VASS
S2VASS
S2VASS
S0RKS
S0RKS
REMRKS
REMRKS
S1RKS
S1RKS
S2RKS
S2RKS
0
5
10
Time (min)
15
20
0
5
Pat 8, MSLT 3
10
Time (min)
15
20
Pat 8, MSLT 4
Arousal
Arousal
Wake
SA+SAF
Wake
SA+SAF
REMVASS
REMVASS
DL
S1VASS
DL
S1VASS
S2VASS
S2VASS
S0RKS
S0RKS
REMRKS
REMRKS
S1RKS
S1RKS
S2RKS
S2RKS
0
5
10
15
Time (min)
20
25
0
5
10
15
Time (min)
20
Figure 10 a. Hypnograms of a patient presenting the four naps. By VASS (above) the repetitive short drop offs and
awakenings starting soon after the onsets of the recordings are visualised. LatS1RKS is rather long (8 min. 22 s) as
compared to LatVASS (2 min. 38 s). For abbreviations see Table 2.
67
Pat 5, MSLT 1
Pat 5, MSLT 2
Arousal
Arousal
Wake
SA+SAF
Wake
SA+SAF
REMVASS
REMVASS
DL
S1VASS
DL
S1VASS
S2VASS
S2VASS
S0RKS
S0RKS
REMRKS
REMRKS
S1RKS
S1RKS
S2RKS
S2RKS
0
5
10
Time (min)
15
20
0
5
Pat 5, MSLT 3
10
Time (min)
15
20
Pat 5, MSLT 4
Arousal
Arousal
Wake
SA+SAF
Wake
SA+SAF
REMVASS
REMVASS
DL
S1VASS
DL
S1VASS
S2VASS
S2VASS
S0RKS
S0RKS
REMRKS
REMRKS
S1RKS
S1RKS
S2RKS
S2RKS
0
5
10
Time (min)
15
20
0
5
10
Time (min)
15
20
Figure 10 b. This patient has short LatS1RKS and LatVASS (2 min. 15 s and 1 min. 21 s respectively). By VASS
the repetitive arousals related to the apneas fragmenting sleep are revealed. By RKS it appears that he is mostly
sound asleep until the end of the naps.
Contr 14, MSLT 1
Contr 14, MSLT 2
Arousal
Arousal
Wake
SA+SAF
Wake
SA+SAF
REMVASS
REMVASS
DL
S1VASS
DL
S1VASS
S2VASS
S2VASS
S0RKS
S0RKS
REMRKS
REMRKS
S1RKS
S1RKS
S2RKS
S2RKS
0
5
10
15
Time (min)
20
25
0
5
10
Contr 14, MSLT 3
15
Time (min)
20
25
Contr 14, MSLT 4
Arousal
Arousal
Wake
SA+SAF
Wake
SA+SAF
REMVASS
REMVASS
DL
S1VASS
DL
S1VASS
S2VASS
S2VASS
S0RKS
S0RKS
REMRKS
REMRKS
S1RKS
S1RKS
S2RKS
S2RKS
0
5
10
15
20
Time (min)
25
30
0
5
10
Time (min)
15
20
Figure 10 c. Hypnograms of a control subject. The differences between RKS and VASS are not as marked as in the
hypnograms of the patients. Short drop offs can be seen before sleep onset can be scored by RKS. More
awakenings can also be seen by VASS than by RKS. The latencies are normal both by RKS (16 min. 38 s) and by
VASS (11 min. 44 s).
68
5.6. RKS / VASS AGREEMENT
The agreement between RKS and VASS was analysed by temporal comparison with
one-second resolution. In the pooled data about 4/5 of S0RKS was scored as clear
wakefulness by VASS if WL, WA, WAF, and EMTW are taken into account as a
single wakefulness entity (Table 12 a). Twelve percent of S0RKS comprised of
drowsiness with SEMs and alpha activity (SA + SAF). About 9 % of the time scored
as S0RKS was scored as sleep (DL, S1VASS, S2VASS + arousals) by VASS.
Conversely approximately 94 % of both WL and WA+WAF was scored as S0RKS
(Table 12 b). Six percent of WL and five percent of WA+WAF were scored as
S1RKS and 0.7 % of WL and 0.4 % of WA+WAF were scored as S2RKS. Eighty-six
percent of SA+SAF was scored as S0RKS and about 14 % as RKS sleep.
Eighteen percent of S1RKS was scored as wakefulness by VASS (WL, WA+WAF,
EMTW, Table 12 a). 7.4 % of S1RKS was scored as alpha-SEMs, indicating only
slight impairment of vigilance. Sixty-four percent was scored identically as S1 (DL
included) by both methods. Seven percent of S1RKS was scored as S2VASS.
Conversely 20 % of S1VASS was scored as S0RKS, 59% of S1VASS was scored as
S1RKS and even 20% as S2RKS (Table 12 b). A remarkable amount of DL (64 %)
was scored as S0RKS and only 34% as S1RKS, even if it should belong to S1RKS by
standard scoring.
Seventy-four percent of S2RKS was scored as S2VASS (Table 12 a). If arousals from
S2VASS are included the percentage increases to 79 %. Eighteen and a half percent of
S2RKS was scored as S1VASS+DL and 1.6 % as wakefulness. Conversely 91 % of
S2VASS was scored as S2RKS, 8 % as S1RKS and 1 % as S0RKS (Table 12 b).
There were a few differences in percentages between the patient and control groups.
In general the agreements were higher in the control group. In the patient group 12 %
of S0RKS was scored as DL or S1VASS whereas in the control group the
corresponding percentage was only 4% (Table 12 a). Twenty-one percent of S2RKS
was scored as S1VASS in the patient group whereas in the control group only 12 % of
S2RKS was scored as S1VASS. A higher percentage of S2RKS was scored as
S2VASS in the control group as compared to the patient group (83% vs. 70%). In the
control group only 6% of alpha-SEMs was scored as RKS sleep, whereas in the
patient group the corresponding percentage was 21% (Table 12 b). In the patient
group S1VASS constituted more of S2RKS than in the control group (22 % vs. 16 %).
69
WL
29.8
7.2
0.0
0.6
WL
31.3
9.0
1.3
Patients
S0RKS
S1RKS
REMRKS
S2RKS
Controls
S0RKS
S1RKS
S2RKS
WA+WAF
48.0
9.2
0.5
WA+WAF
41.5
10.4
0.0
0.8
WA+WAF
44.8
10.0
0.0
0.7
SA+SAF
11.2
5.3
0.2
SA+SAF
12.5
8.1
0.0
0.7
SA+SAF
11.9
7.4
0.0
0.5
WL
91.1
8.2
0.0
0.7
100.0
WL
95.8
3.5
0.7
100.0
Patients
S0RKS
S1RKS
REMRKS
S2RKS
sum%
Controls
S0RKS
S1RKS
S2RKS
sum%
WA+WAF
97.5
2.4
0.2
100.0
WA+WAF
90.9
8.4
0.0
0.6
100.0
WA+WAF
94.4
5.2
0.0
0.4
100.0
SA+SAF
94.0
5.7
0.3
100.0
SA+SAF
79.2
19.1
0.0
1.7
100.0
SA+SAF
85.7
13.2
0.0
1.1
100.0
DL
65.3
34.2
0.5
100.0
DL
63.7
34.0
0.0
2.3
100.0
DL
64.0
34.1
0.0
2.0
100.0
DL
1.2
5.0
0.1
DL
5.4
7.8
0.0
0.5
DL
3.3
7.0
0.0
0.4
S1VASS
24.0
60.0
16.0
100.0
S1VASS
18.7
58.7
0.2
22.3
100.0
S1VASS
20.2
59.1
0.2
20.6
100.0
S1VASS
3.0
59.9
12.2
S1VASS
6.7
56.2
36.7
20.7
S1VASS
4.8
57.2
36.7
18.1
Arousal-1
5.9
64.7
29.4
100.0
Arousal-1
28.1
57.3
0.0
14.6
100.0
S2VASS
1.3
7.0
91.7
100.0
S2VASS
0.9
8.6
0.4
90.1
100.0
S2VASS
1.0
8.0
0.2
90.7
100.0
S2VASS
0.2
8.4
83.2
Arousal-1
0.0
0.3
0.1
Arousal-1
26.1
58.0
0.0
16.0
100.0
S2VASS
0.3
6.8
55.0
69.6
S2VASS
0.2
7.2
55.0
73.8
Arousal-1
0.1
0.9
0.0
0.2
Arousal-1
0.0
0.7
0.0
0.2
Arousal-2
35.3
22.2
42.5
100.0
Arousal-2
19.1
19.3
0.0
61.6
100.0
Arousal-2
21.5
19.7
0.0
58.8
100.0
Arousal-2
0.2
1.2
1.7
Arousal-2
0.9
2.1
0.0
6.5
Arousal-2
0.6
1.9
0.0
5.0
0.0
REMVASS
REMVASS
0.0
0.0
100.0
0.0
100.0
REMVASS
0.0
0.0
100.0
0.0
100.0
REMVASS
0.0
0.0
0.0
REMVASS
0.0
0.0
8.3
0.0
REMVASS
0.0
0.0
8.3
0.0
EMTW
97.3
2.6
0.1
100.0
EMTW
95.4
4.1
0.0
0.5
100.0
EMTW
96.6
3.1
0.0
0.3
100.0
EMTW
4.6
1.0
0.0
EMTW
2.7
0.3
0.0
0.0
EMTW
3.7
0.5
0.0
0.0
EMTS
34.3
28.3
37.4
100.0
EMTS
34.0
22.3
0.0
43.7
100.0
EMTS
34.2
25.2
0.0
40.6
100.0
EMTS
0.1
0.7
0.7
EMTS
0.1
0.2
0.0
0.4
EMTS
0.1
0.3
0.0
0.5
For most abbreviations see Table 2. Arousal-1, all arousals from S1VASS; Arousal-2, all arousals from S2VASS; EMTW, movements and EMG augmentation in wakefulness; EMTS, movements and EMG
augmentation in sleep.
WL
93.5
5.8
0.0
0.7
100.0
Pooled data
S0RKS
S1RKS
REMRKS
S2RKS
sum%
Table 12 b: Percentages of RKS stages in VASS stages
WL
30.6
7.7
0.0
0.8
Pooled data
S0RKS
S1RKS
REMRKS
S2RKS
Table 12 a: Percentages of VASS stages in RKS stages
sum%
100.0
100.0
100.0
sum%
100.0
100.0
100.0
100.0
sum%
100.0
100.0
100.0
100.0
5.7. PERIODICITIES AT SLEEP ONSET
The hypnograms reveal the vigilance stages to alternate periodically. In order to see
whether these fluctuations have different dominant frequencies in different vigilance
states, the periodicities of S1, S2 and episodes with SEMs were studied. One period
was defined from the beginning of the lower vigilance state to the end of the
subsequent higher vigilance state provided that it was followed by the same lower
vigilance state (for stages see Table 2). The data were collected into bins of 2 s and 10
s. S1 periodicity comprised of fluctuation between S1VASS and wake + DL. Eightyone percent of the periods in both groups were < 60 s. In the patient group 59 % of the
periods were < 30 s and in the controls the percentage was 60 %. Both groups had a
great deal of S1 period intervals of less than 20 s (44 % in the patient group, 46 % in
the control group). Patients had a dominant interval of 10 - 20 s, whereas the controls
had more < 10 s intervals. Further analysis with a bin of 2 s revealed a dominant
interval of 7 - 10 s for patients and 9 - 10 s for controls (Figure 11 a).
9
8
7
6
5
Patients
4
Controls
3
2
1
0
Interval length (s)
Figure 11 a. Percentage distributions of period intervals between S1VASS and wake + DL. Data presented in 2 s
bins. Both groups have mostly short intervals. Patients have a dominant peak interval of 7 - 10 s and controls a
peak of 9 - 10 s.
71
20
18
16
14
12
10
Patients
Controls
8
6
4
2
0
Interval length (s)
Figure 11 b. Percentage distributions of period intervals between S2VASS and higher arousal states. Data
presented in 10 s bins. Patients have a dominance of 31 - 60 s intervals and controls have a peak of 41 - 50 s and
another maximum of 81 - 90 s.
10
9
8
7
6
5
Patients
Controls
4
3
2
1
0
Interval length (s)
Figure 11 c. Percentage distributions of period intervals between SEM episodes (SA + SAF + DL) and
wakefulness (WA + WL + WAF). Data presented in 2 s bins. The dominant interval of SEM periodicity is 9 - 10 s
in the patient group and 7 - 8 s in the control group.
72
S2 periodicity was defined as fluctuation between S2VASS and higher vigilance
states. 52 % of the periods in the patient group were < 60 s and 9 % < 30 s. In the
control group 24 % of the periods were < 60 s whereas 6 % were < 30 s. Patients had
a clear dominance of 31 - 60 s intervals whereas controls had a peak of 41 - 50 s and
another maximum of 81 - 90 s (Figure 11 b).
SEM periodicity consisted of fluctuation between SA + SAF + DL and wakefulness
(WA + WL + WAF). Eighty-five percent of the periods were < 60 s in the patients, 63
% being < 30 s. In the control group the percentages were 87 % and 64 %,
respectively. The peak interval of SEM periodicity was 9 - 10 s in the patient group
and 7 - 8 s in the control group (Figure 11 c).
5.8. PSYCHOMETRIC TESTS
5.8.1. Comparison between patient and control groups
The data from the psychometric tests with the comparison between patient and control
groups is presented in Table 13. Patients tended to have longer average response times
in the vigilance test (Vigil). The difference in response times between the third and
first part of the vigilance test also tended to be greater in patients (VIGdiff). Patients
had few more misses in the reaction time test (RT-miss). However, none of these
differences between the groups was statistically significant. Surprisingly, the mean
reaction time in the reaction time test (RT-test) tended to be faster in the patient
group, but this difference was not significant, either.
The Quality of Life Index (QOL) tended to be higher in the control group, indicating
better life quality, but again the difference did not reach statistical significance.
Patients had higher scores in the anxiety scale (SAS), in the depression scale (SDS),
in the Pittsburgh Sleep Quality Index (PSQI) and in the modified Epworth Sleepiness
Scale (mESS). The results indicate that patients tended to be more anxious, depressive
and sleepy and that their subjective sleep quality was poorer, but these differences did
not reach statistical significance. Surprisingly, the scale for subjective well-being
(Bf-S) showed slightly lower values, which means higher subjective well-being for
patients, but the difference was not significant. Controls showed somewhat, but not
significantly better performance in the Alphabetical Cancellation Test (AD-test). In
Digit span the groups performed quite equally. The only test that showed a
statistically significant difference between groups was the Gruenberger Fine-MotorTest for psychomotor activity (FM-test) where, unexpectedly, the patient group
performed better.
73
Table 13. Psychometric comparison between patients and controls
Test
Vigil (s)
VIGdiff (s)
RT-test (s)
RT-miss
QOL
SAS
SDS
PSQI
mESS
FM-test
Bf-S
AD-test
Digit span
Patient group
Median
Range
0.469
0.049
0.532
5
79
27
29
4
8
87
5
462
12
Control group
Median
Range
0,358 - 1,235
-0,006 - 0,227
0,407 - 0,707
0 - 67
23 - 92
23 - 41
22 - 57
3 - 13
2 - 13
59 - 114
0 - 94
420 - 558
8 - 15
0.411
0.025
0.597
1
90
21
25
3
3
65
9
483
12
0,372 - 0,598
-0,020 - 0,383
0,506 - 0,759
0-6
52 - 95
21 - 31
21 - 28
0-5
1-7
43 - 95
0 - 38
301 - 674
8 - 15
p-value
0.35
0.68
0.14
0.21
0.19
0.055
0.055
0.055
0.11
0.040
0.69
0.87
0.96
Vigil, mean response time in vigilance test; VIGdiff, difference in the means of the response times between the
third and the first part of the vigilance test; RT-test, mean reaction time in reaction time test; RT-miss, number of
misses in reaction time test; QOL, Quality of Life Index; SAS, Zung Anxiety Scale; SDS, Zung Depression Scale;
PSQI, sleeping quality index; mESS, modified Epworth Sleepiness Scale; FM-test, Fine-Motor-Test; Bf-S,
well-being test; AD-test, Alphabetical Cancellation Test; Digit span, test for numerical memory.
Comparison between groups by the Mann-Whitney U-test.
5.8.2. Correlation coefficient analysis
The latencies derived from the MSLTs and the parameters derived from nocturnal
recordings as well as psychometric tests were subjected to the Spearman correlation
coefficient analysis. The correlation coefficients are shown in Table 14. AHI and
ODI4 correlated negatively and quite strongly with all latencies. SaO2min showed a
moderate positive association with all latencies, and SDS showed moderate negative
relationship to all latencies.
In addition LatS1RKS showed moderate negative correlations with TST, %S2, RTmiss and FM-test. LatDL showed moderate negative correlations with TST, %S2,
Vigil and RT-miss. LatS1VASS correlated moderately positively with %S4 and QOL
and moderately negatively with %S2, AwakeI, mARI and PSQI. LatVASS showed
moderate negative relationships to %S2, Vigil, RT-miss, PSQI and FM-test. LatHYP
showed moderate negative associations to RT-miss, SAS, PSQI and FM-test. Of all
cumulative and continuous latencies LatCum30 had moderate negative associations to
%S2, ShInd, Vigil, RT-miss, SAS, and PSQI and a moderate positive association to
QOL. LatSEM had highest number of significant correlations with all the nocturnal
and psychometric parameters (11). LatSEM had moderate positive correlations with
SOL, %S4 and QOL and moderate negative correlations to %S2, SAS, PSQI and
mESS.
74
-0.49
-0.20
0.10
-0.57
0.34
-0.41
-0.61
-0.45
-0.50
-0.52
-0.06
0.00
-0.11
Vigil
VIGdiff
RT-test
RT-miss
QOL
SAS
SDS
PSQI
mESS
FM-test
Bf-S
AD-test
Digit span
-0.56
-0.23
0.06
-0.66
0.39
-0.43
-0.61
-0.48
-0.40
-0.49
-0.10
0.01
-0.05
-0.52
0.34
-0.83
-0.47
0.19
-0.52
0.17
0.43
-0.15
-0.44
-0.18
0.04
-0.25
-0.80
0.60
-0.35
-0.29
0.23
-0.45
0.58
-0.50
-0.63
-0.54
-0.37
-0.50
-0.19
0.07
0.12
-0.27
0.27
-0.85
-0.17
0.07
-0.65
0.20
0.52
0.18
-0.38
-0.53
-0.37
-0.52
-0.75
0.58
LatDL LatS1VASS
For abbreviations see Table 3 and Table 13.
-0.54
0.32
-0.84
-0.46
0.22
-0.55
0.15
0.38
-0.07
-0.44
-0.27
-0.03
-0.29
-0.84
0.65
TST
SOL
AHI
SEI
%S1
%S2
%S3
%S4
%SREM
ShInd
AwakeI
ASDARI
mARI
ODI4
SaO2min
LatS1RKS
-0.51
-0.25
0.18
-0.64
0.39
-0.45
-0.60
-0.55
-0.32
-0.54
-0.10
0.03
-0.02
-0.41
0.41
-0.89
-0.39
0.10
-0.57
0.28
0.47
-0.03
-0.48
-0.33
-0.16
-0.37
-0.86
0.66
LatVASS
-0.50
-0.34
0.26
-0.53
0.38
-0.52
-0.60
-0.56
-0.45
-0.58
-0.01
-0.05
-0.23
-0.50
0.44
-0.95
-0.46
0.05
-0.48
0.20
0.36
0.05
-0.48
-0.39
-0.20
-0.36
-0.89
0.65
LatHYP
Table 14. Spearman rank correlation coefficients of study variables
-0.51
-0.27
0.17
-0.62
0.44
-0.49
-0.63
-0.55
-0.29
-0.50
-0.14
0.03
-0.01
-0.38
0.38
-0.88
-0.36
0.04
-0.51
0.25
0.44
0.01
-0.51
-0.38
-0.19
-0.33
-0.83
0.63
-0.54
-0.32
0.20
-0.55
0.49
-0.49
-0.64
-0.54
-0.27
-0.41
-0.24
0.15
-0.01
-0.43
0.34
-0.86
-0.37
0.17
-0.44
0.05
0.41
-0.06
-0.53
-0.28
-0.05
-0.32
-0.81
0.65
-0.47
-0.30
0.18
-0.53
0.44
-0.46
-0.62
-0.53
-0.38
-0.46
-0.13
0.10
-0.05
-0.49
0.44
-0.85
-0.43
0.15
-0.51
0.13
0.44
-0.07
-0.44
-0.27
-0.05
-0.28
-0.84
0.70
-0.46
-0.18
0.17
-0.48
0.37
-0.39
-0.55
-0.47
-0.43
-0.53
-0.07
0.06
-0.05
-0.50
0.31
-0.82
-0.41
0.25
-0.56
0.14
0.41
-0.10
-0.54
-0.25
-0.12
-0.35
-0.81
0.66
-0.58
-0.34
0.08
-0.55
0.54
-0.56
-0.66
-0.64
-0.39
-0.41
-0.27
0.16
-0.02
-0.43
0.38
-0.83
-0.39
0.20
-0.52
0.13
0.45
-0.10
-0.56
-0.24
-0.12
-0.33
-0.83
0.73
-0.58
-0.18
0.04
-0.59
0.39
-0.44
-0.63
-0.50
-0.35
-0.47
-0.19
0.06
-0.08
-0.44
0.26
-0.80
-0.41
0.27
-0.48
0.16
0.32
-0.14
-0.59
-0.22
-0.10
-0.33
-0.81
0.67
-0.56
-0.27
0.09
-0.63
0.41
-0.44
-0.61
-0.50
-0.33
-0.42
-0.16
0.07
-0.03
-0.48
0.36
-0.81
-0.44
0.13
-0.42
0.12
0.37
-0.14
-0.49
-0.20
0.04
-0.21
-0.80
0.65
-0.46
-0.26
0.18
-0.53
0.44
-0.52
-0.66
-0.62
-0.41
-0.47
-0.14
0.11
-0.06
-0.48
0.54
-0.85
-0.45
0.13
-0.54
0.18
0.49
-0.11
-0.45
-0.29
-0.07
-0.26
-0.91
0.76
LatCum5 LatCum10 LatCum15 LatCum20 LatCum30 LatCum60 LatCon5 LatCon10
-0.53
-0.21
0.04
-0.54
0.39
-0.45
-0.63
-0.42
-0.51
-0.46
-0.13
0.02
-0.10
-0.54
0.17
-0.78
-0.46
0.29
-0.41
0.00
0.23
-0.10
-0.52
-0.22
-0.01
-0.28
-0.76
0.64
LatCon15
-0.22
-0.44
0.20
-0.18
0.65
-0.58
-0.59
-0.65
-0.62
-0.35
-0.14
0.09
-0.08
-0.42
0.58
-0.71
-0.30
-0.01
-0.65
0.14
0.61
0.07
-0.05
-0.37
-0.20
-0.30
-0.72
0.59
LatSEM
LatS1RKS showed the strongest correlation of all latencies with TST (together with
LatCon15). LatDL showed the strongest correlation to RT-miss. LatS1VASS showed
strongest correlations with %S2 (together with LatSEM), AwakeI and mARI. LatHYP
showed the strongest correlation with AHI and FM-test. LatCum30 showed the
strongest associations to Vigil and SDS. LatSEM showed the strongest associations to
SOL, %S2 (together with LatS1VASS), %S4, QOL, SAS, PSQI and mESS.
Of nocturnal parameters SEI, %S1, %S3 and %SREM did not show marked
correlations to any of the latencies. VIGdiff, RT-test, Bf-S, AD-test and Digit span did
not show marked associations, either.
5.8.3. Stepwise binary logistic regression analysis
The subjects were divided into two groups defined by MSLT latencies. Cut-off points
of 3 min. and 5 min. were used to divide the subjects into “sleepy“ and “normal“
groups. LatS1RKS, LatDL, LatS1VASS and LatVASS were selected one at a time as
dependent variables. The parameters from nocturnal recordings and psychometric data
were selected as independent variables for stepwise binary logistic regression
analysis. No reasonable models could be derived from this trial.
5.9. REPEATABILITY OF VASS
The author re-scored 8 naps blindly. The percentage of time with identical scorings
ranged from 75.4 to 89.3 % the median being 83.7 %. The values were quite similar
for patients and controls. The percentages ranged between 76.8 and 89.0 % in the naps
of the patients and between 75.4 and 89.3 % in the naps of the control subjects. The
hypnograms of two naps are presented in Figure 12.
76
0
-5
- 10
- 15
- 20
- 25
- 30
- 35
- 40
- 45
- 50
- 55
- 60
0
-5
- 10
- 15
- 20
- 25
- 30
- 35
- 40
- 45
- 50
- 55
- 60
0
-5
- 10
- 15
- 20
- 25
- 30
- 35
- 40
- 45
- 50
- 55
- 60
0
-5
- 10
- 15
- 20
- 25
- 30
- 35
- 40
- 45
- 50
- 55
- 60
Figure 12. Two naps which have been re-scored with a one-year interval. In general the hypnograms look quite
similar. The scoring repeatability between the upper hypnograms is 80.6 % and 85.0 % between the lower
hypnograms.
77
5.10. SPECTRAL DATABASE
Altogether 14:46:30 (h:min:s) of data was analysed in the FFT part of the study. In
RKS 10:20:30 of S0RKS, 2:10:00 of S1RKS and 2:16:00 of S2RKS were studied. By
VASS the amounts of the stages later analysed in more detail by FFT were, 5:11:03 of
WA, 1:04:51 of WAF, 1:17:48 of SA, 0:21:42 of SAF, 2:02:51 of WL, 0:19:56 of DL,
1:57:15 of S1VASS, 1:33:28 of S2VASS, 0:04:01 for Arousal-alpha2 (Aa2), 0:08:59
for EMGW and 0:03:19 for MTW. These VASS stages comprised altogether
14:05:16. The rest of the time, approximately 40 minutes, consisted mainly of other
arousals and movements. The control subject with poor alpha had 1:12:30 of S0RKS,
0:07:30 of S1RKS, 0:23:40 of WA, 0:19:30 of WL and 0:06:23 of S1VASS.
Table 15. Duration and number of segments of each stage analysed by FFT
Total duration
hh:mm:ss
Number of segments
S0RKS
S1RKS
S2RKS
10:20:30
2:10:00
2:16:00
1241
260
272
WA
WAF
SA
SAF
WL
DL
S1VASS
S2VASS
Aa2
EMGW
MTW
5:11:03
1:04:51
1:17:48
0:21:42
2:02:51
0:19:59
1:57:15
1:33:28
0:04:01
0:08:59
0:03:19
2551
1023
849
327
1826
309
545
65
33
185
32
For abbreviations see Table 2.
78
5.11. SPECTRAL COMPARISON BETWEEN RKS AND VASS
5.11.1. Differentiation between wakefulness and S1 by alpha and delta-theta power
bands
An important goal of the FFT part of this study was to see how wakefulness and light
sleep could be differentiated with spectra derived from RKS and VASS stages. In this
respect attention was paid to the differences between alpha (7.42 – 12.88 Hz) and
delta-theta (1.95 – 6.64 Hz) power in wakefulness and S1. The EEG derivation C4M1 was used for comparisons between RKS and VASS.
Alpha power was significantly lower in C4S0RKS than in C4WA but higher in
C4S0RKS than in C4WL (Table 16). There was significantly more alpha in C4S1RKS
than in C4S1VASS. As expected, there was significantly more alpha in C4WA than in
C4S1VASS and in C4S0RKS than in C4S1RKS. Alpha power was lower in C4WL
than in C4S1VASS. If alpha band power is used to discriminate between wakefulness
and light sleep stages, the discriminative power of C4WA – C4S1VASS is better than
the one of C4S0RKS – C4S1RKS. RKS differentiates better between wakefulness and
light sleep when C4WL - C4S1VASS is used in comparison.
Table 16. Comparison between RKS and VASS by alpha power
Median
2
µV
C4S0RKS
C4S1RKS
C4WA
C4WL
C4S1VASS
Alpha power
Min
Max
p-value
8.36
6.07
8.99
4.83
5.14
4.63
4.57
5.89
3.53
3.76
18.38
9.57
13.43
8.05
8.54
C4S0RKS-C4WA
C4S0RKS-C4WL
C4S1RKS-C4S1VASS
C4S0RKS-C4S1RKS
C4WA-C4S1VASS
C4WL-C4S1VASS
-0.39
3.29
1.06
1.69
3.55
-0.26
-2.67
1.02
0.23
-0.52
-0.33
-3.00
6.97
12.56
2.93
9.29
7.09
1.11
0.001
< 0.001
< 0.001
< 0.001
< 0.001
0.02
(C4S0RKS-C4S1RKS)-(C4WA-C4S1VASS)
(C4S0RKS-C4S1RKS)-(C4WL-C4S1VASS)
-1.51
2.13
-4.98
0.09
5.04
10.63
< 0.001
< 0.001
Wilcoxon test for paired differences.
79
Delta-theta power was significantly higher in C4S0RKS than in C4WA or C4WL
(Table 17). There was no significant difference in delta-theta power between
C4S1RKS and C4S1VASS. There was more delta-theta activity in C4S1RKS than in
C4S0RKS. In VASS there was significantly more delta-theta activity in C4S1VASS
than in C4WA or C4WL. The difference in delta-theta power between wakefulness
and sleep stages was higher by VASS than by RKS regardless of the VASS
wakefulness stage (C4WA or C4WL).
The difference in alpha and delta-theta power between wakefulness and S1 was
calculated for both RKS and VASS also for the low-alpha control subject. No
statistical differences were found among C4S0RKS-C4WA, C4S0RKS-C4WL,
C4S1RKS-C4S1VASS, C4S1RKS-C4S0RKS, C4S1VASS-C4WA and C4S1VASSC4WL.
Table 17. Comparison of RKS and VASS by delta-theta power
Delta-thetapower
Median
Min
2
µV
C4S1RKS
C4S0RKS
C4S1VASS
C4WA
C4WL
Max
p-value
6.80
6.06
6.93
5.70
5.25
4.24
3.81
4.37
3.59
3.57
10.71
23.13
12.45
12.03
9.36
C4S0RKS-C4WA
C4S0RKS-C4WL
C4S1RKS-C4S1VASS
C4S1RKS-C4S0RKS
C4S1VASS-C4WA
C4S1VASS-C4WL
0.41
0.97
-0.02
0.86
1.11
1.91
-0.16
-1.32
-1.87
-13.63
-3.62
-0.96
14.31
17.73
0.67
2.71
5.22
5.95
< 0.001
< 0.001
0.23
0.014
< 0.001
< 0.001
(C4S1RKS-C4S0RKS)-(C4S1VASS-C4WA)
(C4S1RKS-C4S0RKS)-(C4S1VASS-C4WL)
-0.73
-1.05
-13.69
-17.12
0.52
0.70
< 0.001
< 0.001
Wilcoxon test for paired differences.
80
5.11.2. Topographical differences in alpha and delta-theta power bands
Topographical differences in alpha and delta-theta power bands between wakefulness
and S1 were analysed for both RKS and VASS. Significant differences in alpha power
were found in S0RKS between all three EEG derivations (Table 18). Post hoc analysis
showed that alpha power was highest occipitally and lowest frontally. In S1RKS the
Friedman test also showed topographical differences in alpha power. No differences
in alpha power was found between the occipital and central regions, whereas power
was lowest frontally. Topographical differences were also found in alpha power
differences between wakefulness and S1. Best spectral wake-sleep discrimination was
obtained by O2. No difference in this respect was found between C4 and Fp2.
Table 18. Topographical analysis of alpha band power in RKS
Alpha power
Median
Min
µV2
S0RKS
C4S0RKS
O2S0RKS
Fp2S0RKS
Friedman test
8.36
9.93
7.83
4.63
6.42
4.28
Max
p-value
Bonferroni
p-value
18.38
21.08
12.52
< 0.001
C4S0RKS-O2S0RKS
C4S0RKS-Fp2S0RKS
O2S0RKS-Fp2S0RKS
S1RKS
C4S1RKS
O2S1RKS
Fp2S1RKS
Friedman test
-1.89
0.63
2.23
-6.23
-2.29
0.27
1.37
7.21
9.10
6.07
6.43
5.37
4.57
4.33
3.91
9.57
10.25
9.26
< 0.001
< 0.001
< 0.001
< 0.001
C4S1RKS-O2S1RKS
C4S1RKS-Fp2S1RKS
O2S1RKS-Fp2S1RKS
Discrimination between S0RKS and S1RKS
C4S0RKS-C4S1RKS
O2S0RKS-O2S1RKS
Fp2S0RKS-Fp2S1RKS
Friedman test
-0.25
0.48
0.88
-1.98
-0.04
-0.70
0.99
1.80
2.51
0.69
3.87
1.82
-0.52
-0.08
-0.43
9.29
14.07
6.14
0.40
< 0.001
< 0.001
< 0.001
(C4S0RKS-C4S1RKS)-(O2S0RKS-O2S1RKS)
(C4S0RKS-C4S1RKS)-(Fp2S0RKS-Fp2S1RKS)
(O2S0RKS-O2S1RKS)-(Fp2S0RKS-Fp2S1RKS)
-1.37
0.13
1.42
-6.21
-2.53
-0.63
0.76
7.10
8.91
< 0.001
0.83
< 0.001
Friedman test for multiple comparisons. Post hoc analysis by Wilcoxon test for paired differences with Bonferroni correction.
81
In WA significant topographical differences in alpha power were observed; power
was highest occipitally and lowest frontally (Table 19). In WL no differences in alpha
power topography were obtained. In S1VASS significant differences were present.
Alpha power in C4 was higher than in Fp2, but there were no significant differences
between C4-O2 and O2-Fp2. With WA the best spectral discrimination for
wakefulness and S1 stages was obtained by O2 while the discriminative value of Fp2
was lowest.
Table 19. Topographical analysis of alpha band power in VASS
Alpha power
Median
µV2
Min
Max
5.89
9.08
4.87
13.43
19.40
13.83
WA
C4WA
O2WA
Fp2WA
Friedman test
8.99
11.71
8.16
C4WA-O2WA
C4WA-Fp2WA
O2WA-Fp2WA
-3.90
0.80
4.41
-6.40
-2.05
0.65
0.93
2.25
6.95
WL
C4WL
O2WL
Fp2WL
Friedman test
4.83
4.81
4.97
3.53
3.56
3.53
8.05
7.50
9.73
S1VASS
C4S1VASS
O2S1VASS
Fp2S1VASS
Friedman test
5.14
4.89
4.60
C4S1VASS-O2S1VASS
C4S1VASS-Fp2S1VASS
O2S1VASS-Fp2S1VASS
0.32
0.28
0.09
-1.28
-0.32
-1.40
2.05
1.01
1.27
Discrimination between WA and S1VASS
C4WA-C4S1VASS
O2WA-O2S1VASS
Fp2WA-Fp2S1VASS
Friedman test
3.55
6.90
3.16
-0.33
4.26
-0.22
7.09
12.47
7.07
(C4WA-C4S1VASS)-(O2WA-O2S1VASS)
(C4WA-C4S1VASS)-(Fp2WA-Fp2S1VASS)
(O2WA-O2S1VASS)-(Fp2WA-Fp2S1VASS)
-4.02
0.61
4.11
p-value
Bonferroni
p-value
< 0.001
< 0.001
0.001
< 0.001
0.097
3.76
3.72
3.28
8.54
7.71
8.57
0.005
0.06
< 0.001
1.00
< 0.001
-7.65
-2.45
0.27
0.47
1.36
8.35
< 0.001
0.008
< 0.001
Friedman test for multiple comparisons. Post hoc analysis by Wilcoxon test for paired differences with Bonferroni correction.
82
Topographical differences in delta-theta power in S0RKS were statistically significant
and the post hoc analysis showed that the delta-theta power in S0RKS was highest
frontally and lowest occipitally (Table 20). In S1RKS delta-theta power was higher in
C4 than in O2 (Figure 13), but the differences between C4-Fp2 and Fp2-O2 were not
significant. Both C4 and O2 differentiated between wakefulness and sleep stages
better than Fp2 whereas there was no difference in discriminative power between C4
and O2.
Table 20. Topographical analysis of delta-theta band power in RKS
Delta-theta power
Median
Min
µV2
S0RKS
C4S0RKS
O2S0RKS
Fp2S0RKS
Friedman test
6.06
5.73
8.39
3.81
3.67
5.89
Max
p-value
Bonferroni
p-value
23.13
20.93
22.64
< 0.001
C4S0RKS-O2S0RKS
C4S0RKS-Fp2S0RKS
O2S0RKS-Fp2S0RKS
S1RKS
C4S1RKS
O2S1RKS
Fp2S1RKS
Friedman test
0.72
-1.89
-2.42
-4.00
-14.01
-16.38
7.04
11.26
7.12
6.80
7.01
6.63
4.24
4.24
4.60
10.71
9.62
11.08
0.044
< 0.001
< 0.001
0.009
C4S1RKS-O2S1RKS
C4S1RKS-Fp2S1RKS
O2S1RKS-Fp2S1RKS
0.65
0.02
-0.37
-1.40
-3.95
-4.26
2.58
1.46
1.57
Discrimination between S1RKS and S0RKS
C4S1RKS-C4S0RKS
O2S1RKS-O2S0RKS
Fp2S1RKS-Fp2S0RKS
Friedman test
0.86
0.73
-1.21
-13.63
-13.94
-15.55
2.71
2.72
1.87
(C4S1RKS-C4S0RKS)-(O2S1RKS-O2S0RKS)
(C4S1RKS-C4S0RKS)-(Fp2S1RKS-Fp2S0RKS)
(O2S1RKS-O2S0RKS)-(Fp2S1RKS-Fp2S0RKS)
-0.12
1.55
1.26
0.011
1.00
0.09
< 0.001
-6.85
-11.56
-8.24
6.59
15.29
16.64
1.00
< 0.001
0.001
Friedman test for multiple comparisons. Post hoc analysis by Wilcoxon test for paired differences with Bonferroni correction.
11
10
S1RKS: Delta-theta power in O2
9
8
7
6
5
4
4
5
6
7
8
9
S1RKS: Delta-theta power in C4
Figure 13. In S1RKS delta-theta power was higher in C4 than in O2.
83
10
11
Both in WA and in WL the power of delta-theta band was significantly highest
frontally and lowest occipitally (Table 21). In S1VASS there was a significant
difference in delta-theta power between C4-O2, presenting more delta-theta power
centrally (Figure 14). The differences between C4-Fp2 and O2-Fp2 were not
significant. Central and occipital derivations differentiated equally well between
wakefulness (both WA and WL) and S1VASS, both better than the frontal derivation.
Table 21. Topographical analysis of delta-theta band power in VASS
Delta-theta power
Median
µV2
Min
Max
WA
C4WA
O2WA
Fp2WA
Friedman test
5.70
5.35
7.80
3.59
3.68
4.09
12.03
9.92
35.00
C4WA-O2WA
C4WA-Fp2WA
O2WA-Fp2WA
0.62
-1.82
-2.20
-1.27
-22.97
-26.66
3.69
0.09
1.01
WL
C4WL
O2WL
Fp2WL
Friedman test
5.25
4.62
7.80
3.57
3.23
5.01
9.36
8.26
38.85
C4WL-O2WL
C4WL-Fp2WL
O2WL-Fp2WL
0.77
-2.96
-2.97
-1.20
-29.49
-33.08
3.59
0.32
0.03
S1VASS
C4S1VASS
O2S1VASS
Fp2S1VASS
Friedman test
6.93
7.02
6.90
4.37
4.10
4.70
12.45
10.36
11.09
p-value
Bonferroni
p-value
< 0.001
0.003
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
0.004
C4S1VASS-O2S1VASS
C4S1VASS-Fp2S1VASS
O2S1VASS-Fp2S1VASS
0.67
0.22
-0.19
-1.52
-0.86
-2.50
2.85
2.23
1.46
Discrimination between S1VASS and WA
C4S1VASS-C4WA
O2S1VASS-O2WA
Fp2S1VASS-Fp2WA
Friedman test
1.11
0.90
-0.63
-3.62
-1.57
-26.58
5.22
3.36
3.03
(C4S1VASS-C4WA)-(O2S1VASS-O2WA)
(C4S1VASS-C4WA)-(Fp2S1VASS-Fp2WA)
(O2S1VASS-O2WA)-(Fp2S1VASS-Fp2WA)
0.03
1.90
1.57
-3.34
0.17
-0.29
1.86
22.96
26.29
Discrimination between S1VASS and WL
C4S1VASS-C4WL
O2S1VASS-O2WL
Fp2S1VASS-Fp2WL
Friedman test
1.91
2.12
-0.79
-0.96
-0.68
-32.05
5.95
4.12
3.80
(C4S1VASS-C4WL)-(O2S1VASS-O2WL)
(C4S1VASS-C4WL)-(Fp2S1VASS-Fp2WL)
(O2S1VASS-O2WL)-(Fp2S1VASS-Fp2WL)
-0.10
2.70
2.84
0.002
0.065
0.52
< 0.001
1.00
< 0.001
< 0.001
< 0.001
-2.43
0.15
-0.85
2.00
31.08
33.51
Friedman test for multiple comparisons. Post hoc analysis by Wilcoxon test for paired differences with Bonferroni correction.
84
1.00
< 0.001
< 0.001
14
13
12
S1VASS: delta-theta power in O2
11
10
9
8
7
6
5
4
4
6
8
10
12
14
S1VASS: Delta-theta power in C4
Figure 14. In S1VASS delta-theta power was higher in C4 than in O2.
5.11.3. Peak frequencies of RKS and VASS stages
The spectral peak frequencies and the powers of the peaks of different RKS and
VASS stages in C4 derivation are shown in Figure 15. The numerical values are
shown in Table 22 and in the Appendix (Table 23 and Table 24). The peak
frequencies were in the alpha range in WA, SA, WAF, SAF and S0RKS, and in deltatheta range in WL, DL, S1VASS, S2VASS, S1RKS and S2RKS. The horizontal bars
in the Figure 15 show statistically significant peak frequency differences between
stages. The peak frequencies of the VASS alpha stages differed significantly from the
peak frequencies of all non-alpha stages. The only significant difference between
VASS alpha stages was between WA and SAF. The VASS alpha stages did not differ
from S0RKS. The peak frequency of S1RKS was in the delta-theta range. It did not,
however, differ significantly from S0RKS but was different from the VASS alpha
stages. As expected, the peaks were prominent in the VASS alpha stages and S0RKS.
The peaks of DL and WL were quite low. Somewhat higher peaks in the theta range
existed in S1VASS and S1RKS. Both S2VASS and S2RKS had prominent slow
peaks.
Table 22. Peak frequencies and power of the peaks in C4 derivation in RKS stages.
Peak frequency
Median
Min
Hz
C4S0RKS
C4S1RKS
C4S2RKS
9.4
3.1
2.3
2.3
2.3
2.3
Power of the peak
Median
Min
2
µV
Max
10.9
10.2
3.9
1.91
1.52
2.10
85
0.99
0.91
1.33
Max
4.80
2.44
2.93
2.5
2.0
S2VASS
DL
S1RKS
S1VASS
SAF
WAF
S0RKS
SA
WA
.5
S2RKS
1.0
WL
Power of the peak (µV2)
1.5
2.3
2.3
2.3
3.1
3.1
3.1
7.8
9.4
9.4
9.4
10.2
Peak frequency (Hz)
WA
SA
S0RKS
WAF
SAF
S1VASS
S1RKS
DL
S2VASS
S2RKS
WL
Figure 15. Peak frequencies and power of the peaks in RKS and VASS stages in C4 derivation. Below the
horizontal bars show statistically significant peak frequency differences between stages. There are significant
differences between VASS wakefulness and sleep stages. On the contrary, the difference between the peak
frequencies of S0RKS and S1RKS was not statistically significant.
(Friedman test for multiple comparisons, post hoc analysis by Wilcoxon test for paired differences with Bonferroni
correction).
86
5.11.4. Summary of differences between RKS and VASS
To visualise the differences between standard scored stages and VASS stages, the
delta-theta and alpha band powers of the other VASS stages were also calculated in
C4. The peak frequencies as well as the delta-theta and alpha powers are summarised
in Figure 16. The following trends are noted, although not all differences are
necessarily statistically significant. There was a decreasing trend in alpha power along
with VASS alpha stages. This was concomitant to a decrease in peak frequencies.
S0RKS resembled most SA. S1RKS and S1VASS differed in alpha power. Deltatheta power was high in both S2RKS and S2VASS as also in S1RKS and S1VASS.
10
8
6
4
2
0
C4 alpha power (µV2)
C4 delta-theta power (µV2)
C4 peak frequency (Hz)
Figure 16. Summary of spectral findings in C4. A decreasing trend in alpha power along with VASS alpha stages
can be seen. This is associated with a concomitant decrease in peak frequency. The spectral properties of S0RKS
are close to SA. A difference in alpha power between S1RKS and S1VASS can be seen, indicating that part of
S1RKS actually consists of wakefulness with alpha activity.
5.12. FREQUENCY BAND POWER CHARACTERISTICS OF VASS STAGES
5.12.1. Peak frequency topography in VASS
Peak frequencies and power of the peaks of VASS stages in Fp2, C4 and O2 are
illustrated in Figures 17 a - c. The numerical values with statistical differences are
presented in Appendix (Table 23 and Table 24). In the following all comparisons are
statistically significant unless otherwise stated.
87
4
3
2.5
2
S2VASS
WL
S1VASS
DL
WAF
SAF
SA
1.5
WA
Median power of the peak (µV2)
3.5
2.3
2.3
2.3
2.3
2.3
6.6
7.0
9.4
1
.5
Peak frequencies in Fp2 (Hz)
4
3
2.5
2
DL
SAF
WAF
SA
WA
.5
S1VASS
1
WL
1.5
S2VASS
Median power of the peak (µV2)
3.5
2.3
2.3
3.1
3.1
7.8
9.4
9.4
10.2
Peak frequencies in C4 (Hz)
4
3
2.5
2
WAF
SAF
WA
2.3
3.1
3.1
3.9
9.4
9.4
10.2 10.2
SA
DL
.5
S1VASS
1
WL
1.5
S2VASS
Median power of the peak (µV2)
3.5
Peak frequencies in O2 (Hz)
Figures 17 a, b and c. Topographical comparison between peak frequencies and power of the peaks of VASS
stages in Fp2, C4 and O2. For description see text.
88
In WA the median peak frequencies were 10.2 Hz in both central and occipital
derivations. However, a statistically significant difference was found, indicating the
peak frequency to be higher occipitally (Figure 18). The median peak frequency was
slowest frontally being almost in the delta range. Peak power was highest occipitally,
but there was no significant difference in power between C4 and Fp2.
12
10
WA: Peak frequency in O2
8
6
4
2
2
4
6
8
10
12
WA: Peak frequency in C4
Figure 18. Peak frequencies were in general higher occipitally than centrally in WA.
In SA all the median peaks were in alpha range. The fastest median peak frequency
appeared occipitally, but no difference between central and frontal derivation was
arisen. The median power of the peak was highest occipitally and lowest frontally
(Figure 19).
3.5
3.0
SA: Power of the peak in Fp2
2.5
2.0
1.5
1.0
.5
.5
1.0
1.5
2.0
2.5
3.0
3.5
SA: Power of the peak in C4
Figure 19. Power of the peak was lower in Fp2 than in C4 in SA.
In WAF central and occipital median peaks were in the alpha range. The median peak
frequency was slowest frontally, actually in the theta range, but no difference was
observed between the central and occipital derivations. The median power of the
peaks was lowest occipitally, no difference was seen in the central and frontal
derivations.
89
In SAF the fastest median peak was obtained occipitally with no significant difference
between central and frontal derivations. The occipital peak was clearly in the alpha
range and central and frontal peaks were in the alpha-theta range. The median power
of the peaks was lowest occipitally with no difference between frontal and central
derivations
In WL peak frequency was highest occipitally and slowest frontally (Figure 20). The
median frequencies were in the delta-theta range. The highest peak power was seen
frontally, whereas no significant differences were found between C4 and O2.
12
10
WL: Peak fequency in Fp2
8
6
4
2
2
4
6
8
10
12
WL: Peak frequency in C4
Figure 20. Peak frequency was lower frontally than centrally in WL.
In DL the median peak frequency was slowest in Fp2. The difference between O2 and
C4 was not significant. The frequencies were all in the delta-theta range. The highest
median peak power was seen frontally, whereas the difference between C4 and O2
was not significant.
In S1VASS all the median peak frequencies were in the delta-theta range and no
significant frequency differences between the leads were observed. The median peak
power was lowest occipitally, whereas the difference between Fp2 and C4 was not
significant.
In S2VASS all median peak frequencies were 2.3 Hz, but a significant difference
showing a faster frequency in O2 than in C4 was obtained (Figure 21). No differences
were found between C4 and Fp2 and O2 and Fp2. The median peak power was lowest
occipitally but there was no significant difference between C4 and Fp2.
5.5
5.0
S2VASS: Peak frequency in O2
4.5
4.0
3.5
3.0
2.5
2.0
2.0
2.5
3.0
3.5
4.0
4.5
5.0
S2VASS: Peak frequency in C4
Figure 21. Peak frequencies were higher occipitally than centrally in S2VASS.
90
5.5
5.12.2. Topographical band power differences within stages
To study spectral characteristics of VASS stages the spectral power was divided into
five frequency bands: delta (0 – 3.51), theta (3.52 – 7.41), alpha (7.42 – 12.88), sigma
(12.89 – 16.01) and beta (16.02 – 24.60). The power of these five frequency bands for
all three EEG derivations in 11 VASS stages are shown in Figures 22 a - k. The
corresponding numerical values with statistical comparison between power of
frequency bands within stages are presented in the Appendix (Table 25). In the
following presentation all differences are statistically significant unless otherwise
stated.
In WA and SA the alpha power was highest occipitally, declining frontally. In WAF,
DL, S1VASS and S2VASS alpha activity was almost evenly distributed. In SAF there
was a central dominance of alpha power. In WL there were no topographical
differences. In arousal (Aa2) there was a centro-occipital maximum. In EMGW and
MTW no topographical differences existed
In WA and WL theta power was highest frontally, declining occipitally. In SA, WAF,
SAF and DL the maxima were fronto-central. In DL lowest power was obtained
occipitally (Figure 23). In S1VASS there was a central dominance and in S2VASS the
maximum was centro-occipital. In Aa2 and MTW no significant topographical
differences were found. In EMGW the power was highest frontally.
10
9
8
7
DL: Thetapower in O2
6
5
4
3
2
1
1
2
3
4
5
6
7
8
9
10
DL: Theta power in C4
Figure 23. Theta power was higher centrally than occipitally in DL.
The delta band power of the frontal channel was high in several stages due to the
effects of eye movements. Frontal dominance was found in WA, SA, WAF, SAF,
WL, DL, S1VASS, S2VASS, Aa2 and EMGW. No topographical differences were
found in MTW.
In S2VASS (as also in WAF, SAF and S1VASS) the sigma band power was highest
centrally. In WL and DL there was a fronto-central maximum. In WA and SA the
maximum was centro-occipital. In Aa2 the sigma power was quite evenly distributed,
although the power was slightly lower frontally than centrally. No differences were
found in EMGW and MTW.
91
WA
12
Median power (µV2)
10
8
6
4
2
0
Fp2
C4
O2
C4
O2
SA
12
Median power (µV2)
10
8
6
4
2
0
Fp2
Figures 22 a and b.
Band powers of delta, theta, alpha, sigma and beta frequency bands of the derivations Fp2, C4 and O2 for WA and
SA are shown.
Alpha power maxima of both stages are located occipitally. Frontal delta and theta powers are higher in WA due to
fast eye movements. Frontal sigma and beta powers are lower in SA than in WA. No other statistically significant
differences were found between stages.
92
WAF
12
Median power (µV2)
10
8
6
4
2
0
Fp2
C4
O2
SAF
12
10
8
6
4
2
0
Fp2
C4
O2
Figures 22 c and d.
Band powers of delta, theta, alpha, sigma and beta frequency bands of the derivations Fp2, C4 and O2 for WAF
and SAF are shown.
Alpha powers are clearly lower than in WA and SA. Occipital dominances of alpha activity have disappeared. The
only statistically significant difference between stages is higher theta power centrally in SAF.
93
WL
12
Median power (µV2)
10
8
6
4
2
0
Fp2
C4
O2
C4
O2
DL
12
Median power (µV2)
10
8
6
4
2
0
Fp2
Figures 22 e and f.
Band powers of delta, theta, alpha, sigma and beta frequency bands of the derivations Fp2, C4 and O2 for WL and
DL are shown.
All band powers are in general low. Due to fast eye movements the delta and theta powers are higher frontally in
WL than in DL. Otherwise the only statistically significant difference between stages is higher theta power
occipitally in DL.
94
S1VASS
12
Median power (µV2)
10
8
6
4
2
0
Fp2
C4
O2
S2VASS
12
Median power (µV2)
10
8
6
4
2
0
Fp2
C4
O2
Figures 22 g and h.
Band powers of delta, theta, alpha, sigma and beta frequency bands of the derivations Fp2, C4 and O2 for S1VASS
and S2VASS are shown.
Delta power is increased in both stages. Delta and theta powers are in general significantly higher in S2VASS than
in S1VASS. Sigma activity is higher centrally in S2VASS than in S1VASS.
95
Aa2
12
Median power (µV2)
10
8
6
4
2
0
Fp2
C4
O2
EMGW
28,21
15,33
15,64
12
Median power (µV2)
10
8
6
4
2
0
Fp2
C4
O2
Figures 22 i and j.
Band powers of delta, theta, alpha, sigma and beta frequency bands of the derivations Fp2, C4 and O2 for Aa2 and
EMGW are shown.
In Aa2 alpha power is increased as compared to S2VASS. Alpha power distribution differs from WA and SA. In
EMGW fairly high powers are seen in all bands.
96
45
MTW
76,93
58,71
40
Median power (µV2)
35
30
25
20
15
10
5
0
Fp2
C4
O2
Figure 22 k. Band powers of delta, theta, alpha, sigma and beta frequency bands of the derivations Fp2, C4 and O2
for MTW are shown.
In MTW all band powers are increased as compared to other stages, but due to wide dispersion the increases are
not always statistically significant. Note the different scale as compared to other figures.
The beta band power maximum in WA and SA was centro-occipital. In WAF and
SAF the maximum was centrally located as also in S2VASS and Aa2. In WL, DL and
S1VASS the maximum was fronto-central. In Aa2 and EMGW the power maximum
was central. No differences were obtained in MTW.
5.12.3. Band power differences between stages
A schematic presentation of the five band power differences in C4 and O2 derivations
between VASS stages is illustrated in Figure 24. Power band comparisons between
VASS stages are presented in Appendix (Table 26). In the following the differences
between EMGW and MTW are not described.
Alpha band power was highest in WA, SA and Aa2. It was lower in WAF and SAF
and lowest in WL and DL and S1VASS. Alpha power increased in S2VASS as
compared to S1VASS.
97
WA
SA
WL
WAF
SAF
DL
S1VASS S2VASS
Aa2
O2 alpha 12
11
10
C4 alpha
9
8
7
6
5
6
C4 theta
O2 theta
5
4
10
9
8
C4 delta
O2 delta
7
6
4
C4 sigma
O2 sigma 3
2
9
C4 beta
O2 beta
8
7
6
5
Figure 24. Schematic presentation of changes in band powers in VASS stages.
Alpha activity is located occipitally in wakefulness. With decrease of vigilance the total alpha power shows a
diminishing trend and the occipital predominance disappears. Theta and delta powers in general show increasing
trend with lowered vigilance. A decreasing trend within drowsy stages is seen in beta power, but in S1VASS and
S2VASS the power increases slightly. All fast band powers increase in Aa2, whereas a clear decrease in delta
power is seen.
98
Theta power was high in WA, SA, SAF, S1VASS and S2VASS. In WAF the theta
power was lower centrally than in SAF. In S1VASS theta power increased as
compared to DL and a further increase was observed in S2VASS.
Centrally and occipitally the delta power was higher in S1VASS than in higher
arousal stages with the exception of WL, which did not differ significantly from
S1VASS. Delta power was higher in S2VASS than in S1VASS but frontally no
significant difference was observed. It is generally known that eye movements
contribute greatly to the delta band power. The power of frontal delta was high in WA
and WL where there are large amounts of fast eye movements. In the corresponding
stages with SEMs (SA, DL) the power of frontal delta was significantly lower. In
WAF the frontal delta power was lower than in WL and WA. No differences in delta
power were observed between SA, WAF and SAF in any leads. No significant frontal
differences between DL and S1VASS or S1VASS and S2VASS were found.
In general, sigma power was high in stages with high alpha activity and low in stages
with low alpha power. Sigma power was higher in S2VASS than in S1VASS with the
exception of Fp2, where the difference was not significant. No differences in sigma
band power were found between DL and S1VASS.
Beta power was lower in WAF and SAF as compared to WA and SA. Beta power was
in general higher in WA, SA and WAF than in WL or DL. The few exceptions were
frontal comparisons between WL-SA and WL-WAF and DL-WAF, which did not
reach statistical significance. WL or DL did not differ markedly from SAF, with the
exception of the occipital comparison between DL-SAF, where more beta was
obtained in SAF. There were no significant differences in beta power between DL and
S1VASS or S1VASS and S2VASS.
5.13. BAND POWER DIFFERENCES BETWEEN ADJACENT VASS STAGES
Power spectra for adjacent stages appeared to greatly resemble each other. In the
following the focus is on the differences between these stages.
5.13.1. WA versus SA
Alpha activity did not show any significant differences between SA and WA, with
occipital power maxima in both stages. In WA the highest theta power was obtained
frontally, in SA centro-frontally. Theta power was in general high in stages with
prominent alpha activity. Delta and theta powers were higher frontally in WA than in
SA. The power maximum of delta band was frontal in both stages. Both sigma and
beta powers were higher frontally in WA than in SA, with centro-occipital maxima.
Stages with prominent alpha activity also showed a great amount of sigma activity.
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5.13.2. WAF versus SAF
In WAF the alpha activity was fairly evenly distributed among derivations but in SAF
a clear central dominance was observed. The amount of alpha power did not,
however, differ significantly in any of the leads between stages. Both in WAF and
SAF the theta activity was fronto-centrally located. In C4 theta power was higher in
SAF than in WAF, other derivations did not indicate significant differences. Delta
activity had frontal dominance both in WAF and in SAF. The amount of delta activity
did not differ between these two stages. Both sigma and beta activities had central
dominance both in WAF and SAF and the powers did not differ in these two stages.
5.13.3. WL versus DL
In WL no topographical differences were found in alpha activity. In DL the alpha
activity was also fairly evenly distributed. The alpha power in WL did not differ from
DL. In WL theta activity had frontal maximum while the maximum was fronto-central
in DL. The amount of theta activity was higher frontally in WL. On the other hand
theta power was higher occipitally in DL than in WL. Delta activity had frontal
maximum in both stages. The only difference in delta power between these stages was
seen frontally, where the amount of delta was higher in WL. Both sigma and beta
activities had fronto-central dominance in both stages. No power differences in beta or
sigma activity were found between WL and DL.
5.13.4. S1VASS versus S2VASS
Topographical distributions of alpha activity in S1VASS and S2VASS were quite
even. Alpha power was slightly but significantly higher in S2VASS than in S1VASS.
The power maximum of the theta activity was centrally located in S1VASS. In
S2VASS more wide centro-occipital maximum was obtained. Theta power was higher
in S2VASS than in S1VASS in all derivations. Delta activity showed frontal
maximum in both S1VASS and S2VASS. While no difference in delta power was
observed frontally, delta activity was higher in S2VASS than in S1VASS in other
derivations. Sigma activity was centrally located in both stages. The amount of sigma
activity was higher in S2VASS than in S1VASS except frontally, where no significant
difference was found. Beta activity had centro-frontal dominance in S1VASS but in
S2VASS the maximum was central. No differences were observed between stages.
5.13.5. Aa2 versus S2VASS
Alpha activity in Aa2 was centro-occipitally located. Alpha power in Aa2 was
significantly higher than in S2VASS. Theta activity in Aa2 was evenly distributed but
power differences were not seen as compared to S2VASS. Delta power had frontal
maximum in Aa2 as in S2VASS. No significant differences due to wide dispersion in
Aa2 were found in delta activity between Aa2 and S2VASS. Sigma power was higher
in Aa2 than in S2VASS, but the maximum was centro-occipital in Aa2 as compared
to central maximum in S2VASS. The trend that sigma power is high in alpha stages
was also seen in Aa2. Beta power was higher in Aa2 than in S2VASS. Beta activity in
Aa2 had central dominance, as in S2VASS.
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6. DISCUSSION
One idea of the present work was to study whether it is possible to increase the number of
the stages by purely morphological definitions based on EEG, EOG and EMG. The MSLT
was chosen for the application for VASS because it is sensitive to small differences in
stage determination and allows a detailed study of the course of sleep onset. In a previous
pilot on VASS, SEMs and topography were not properly taken into account (Himanen et
al. 1999). In the current work two new stages (WAF and SAF) were defined. These are
characterised by alpha activity with no clear occipital dominance. Phasic events were
taken into account as landmarks of stages regardless of the channel in which they
occurred. This resulted in nine sleep stages together with arousals, EMG augmentation
and movements. Arousals, segments with muscle activity and movements have not been
scored separately in any other previous scoring system. As the morphology of alpha
arousal resembles WA or SA concomitant EMG increase or cable artefact was required in
arousal scoring. All recordings could be classified by these stages, even two MSLTs with
poor alpha activity. One control subject did not have DL at all. Previously 13 % of
subjects have been observed not to have Hori’s stage 4 with EEG flattening (Tanaka et al.
1996).
6.1. TEMPORAL RESOLUTION – Does VASS increase the temporal resolution in sleep analysis?
According to a large number of studies it seems evident that more stages are needed in
sleep staging, especially in studies dealing with sleep onset or slight impairment of
vigilance. Hori and co-workers divided sleep onset successfully into 9 different stages
according to the morphology of the EEG signal (Hori et al. 1994). However, different
kinds of eye movements or topographical changes in alpha activity were not included in
this stage classification system. As stated, discrimination between Hori’s wakefulness
stages 1 – 3 with alpha activity could be difficult with low-alpha subjects (Tanaka et al.
1996). In Hori’s method scoring is usually performed in fixed epochs of 5 s, but it has also
been applied in epochs of 30 s (Tanaka et al. 1996, Tanaka et al. 1997).
The need for increase in temporal resolution was already visualised by Morrell (1966). He
examined subjects with eyes closed, using a flash of light as a stimulus. One second of
EEG before the stimulus was classified into one of the three stages. Morrell presented in
an example how 1-2 s before the stimulus the theta pattern EEG changed into alpha
activity the response time being rapid. If a longer epoch was used the result would have
been that the rapid response occurred during sleep. Tanaka et al. (1996) have also
emphasized the importance of increased temporal resolution.
6.1.1. Hypnograms
The increase in temporal resolution as well as the effects of more detailed stage definitions
in the present work are clearly illustrated by the hypnograms (Figures
10 a - c).
Microsleeps with arousals and other brief stage changes, typical of OSAS are revealed, as
are the subtle fluctuations in vigilance. Neither of these phenomena is properly visualised
by RKS hypnograms. Subtle vigilance fluctuations are of importance, when even the
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slightest impairment of alertness can be hazardous, as in traffic. In a recent study nearly all
long-haul truck drivers thought that their driving performance was impaired when driving
while sleepy (Häkkänen et al. 2000). Moreover, about 40 % reported having had problems
in staying alert on at least 20 % of the drives and of these drivers 17 % had experienced a
near miss situation due to lowered vigilance. As stated, MSLT latency is a discrete
measure and cannot reflect fluctuations of sleepiness (Åkerstedt 1988). Instead, with open
eyes, increased alpha and theta activities with SEMs are closely correlated with sleepiness.
As the sleep stages provided by RKS can be seen to be inadequate in revealing these
minor fluctuations in vigilance, more stages are apparently needed.
6.1.2. Quantitative parameters reflecting increased temporal resolution
More naps with sleep could be identified by VASS than by RKS. As shown in Figure 6 b,
some control subjects had 10 - 15 s of continuous sleep even if sleep by RKS was not
reached. These are quite long stretches of sleep and totally ignored by RKS. In some naps
of the controls 20 - 60 s of cumulative sleep was found but RKS sleep was not scored. In
this way the adaptive approach provides more sensitivity to polygraphic recordings in
detecting sleep episodes which remain unnoticed by RKS. It was also of interest to see
that even the controls fell asleep in about 90 % of the naps by VASS. It seems that 20 min.
in a peaceful and dim environment is long enough for even non-sleepy subjects to fall
asleep.
VASS resulted in general in shorter MSLT sleep onset latencies. Statistical comparisons
between latencies show that the length of continuous sleep episodes before reaching
LatS1RKS were shorter in the patient group. This is revealed by the fact that controls had
on average 10 s of continuous sleep before LatS1RKS was reached, whereas in the patient
group only an average 5 s of continuous sleep was reached (Table 8, Table 9). This can be
considered as a marker of fragmentation of sleep in apnea patients.
VASS proved to be better at detecting stage fluctuations revealing more stage shifts during
naps than RKS (Table 6, Table 7). Taking into account sleep microstructure, as in VASS,
a clear differentiation between groups was achieved by stage shifts whereas the number of
stage shifts by RKS did not differ between groups (Table 4).
Significantly less S2 was obtained by VASS than by RKS in the patient group (Table 6,
Figure 7). This implies that some episodes with S2RKS have been scored into other stages
in VASS. This was confirmed by the second by second comparison between RKS and
VASS as only about 70 % of S2RKS was scored as S2VASS (Table 12 a). The rest was
scored as S1VASS, arousals or wakefulness. This trend was also seen in the controls. In
addition, further exploration showed that part of RKS wakefulness was actually sleep and
vice versa. Overlapping was more frequent in the patients having more fragmented sleep.
This means that due to the epoch thinking each RKS stage consists in fact of several
vigilance states. The resulting conventional sleep stage information can be considered
unsatisfactory. This obviously explains why the differences between sleep parameters of
patients and normal healthy subjects sometimes show marked similarities.
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When sleep onset is examined by VASS it is revealed that some stage durations are in
reality very short. Part of S1VASS and wakefulness segments were clearly shorter than
half the epoch required in standard scoring (Figures 9 a and b). In the determination of
sleep onset by RKS these short segments would be undetectable unless there were more
than one segment in a 30 s epoch. By RKS long episodes of several minutes were
common. This strengthens the impression already visualised by hypnograms that in many
cases RKS gives an artificially sound picture of sleep. Both scoring methods gave long S2
segments up to several minutes (Figure 9 c). A considerable proportion of S2VASS
segments was, however, short, being as little as 15 s or less. The most frequent S2RKS
duration was 30 s, which does not reflect the reality because the short segments remained
undetectable or were rounded into 30 s segments.
Tanaka et al. (1996) using Hori’s 9 stage scoring system with 30 s epochs, observed 41%
of their wakefulness stage 1 periods to last 0 - 30 s. As Hori’s stages 2 and 3 consist of
intermittent alpha activity these epochs can be interpreted to consist of at least two
alternating VASS stages with stationary segments of < 30 s regardless of the duration of
the scored segment. Together these patterns of < 30 s comprised 84 % of Hori’s stages 1 3. By VASS an even higher proportion of < 30 s segments was obtained in wakefulness
(96 %). On the other hand, the subjects in the study by Tanaka et al. (1996) were preselected very high-alpha subjects, which may artificially lengthen the periods with alpha
activity as compared to the present data. In any case, part of the difference in segment
length is obviously due to use of adaptive segments instead of 30 s epochs.
In summary, both Figures 10 a - c and the quantitative results indicate that the increased
number of stages together with adaptive approach in scoring increase the temporal
resolution markedly.
6.2. MSLT AND NOCTURNAL PARAMETERS, PSYCHOMETRIC TESTS AND
SUBJECTIVE ASSESSMENT – Is there a better parameter than the epoch scored mean latency of
MSLT to assess sleepiness?
6.2.1. Nocturnal parameters
In general the nocturnal polygraphic findings of the patients were typical for OSAS. Sleep
parameters of the control subjects showed a slight impairment of sleep quality as
compared to previous findings with healthy subjects (Williams et al. 1974).
The differences in nocturnal parameters between the groups were modest. The commonly
used parameters reflecting sleep macrostructure did not distinguish well between patients
and controls. Similar findings have been noted before (see Parrino et al. 2000). CAP
scoring is considered to expose sleep microstructure better than the conventional method.
The presence of CAP is expressed by the CAP ratio. In the present work the nocturnal
arousals were also scored by modified definitions, which strongly resemble the scoring
rules of CAP A phases. However, the arousals were taken into account as events, not as
oscillatory phenomena as in CAP rate. These modified arousals (mARI) did not indicate
significant differences between groups although the median mARI was higher in the
patient group. One reason might be that two control subjects were over 70 years old and
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had a lot of arousals. This concurs with the finding that CAP rate increases with age
(Terzano and Parrino 1993). Another reason for poor differentiation between patients and
controls by mARI might be the fact that control subjects’ TST was lower. This could
reflect the poorer adaptation to the laboratory environment of the control subjects. That
the percentages of sleep stages did not differ between groups can also be due to the two
elderly control subjects who did not have S4 at all, which is a common finding in elderly
people (Feinberg 1974, Webb and Dreblow 1982).
6.2.2. Effects of VASS in clinical evaluation of the MSLT
In the present study the majority of the apnea patients had long or medium MSLT mean
latencies by RKS. This is not surprising, as scoring by epochs ignores short sleep episodes
common in OSAS. When temporal resolution was increased and short sleep spells were
revealed the latencies became shorter. By LatS1VASS and LatDL the majority of patients
received short latencies. Only one of the control subjects received a short latency by
LatDL and her LatS1VASS was very close to normal
(9 min. 58 s). This control
subject was a 77 year old female having an AHI of 7. Although she did not report EDS it
is possible that she had subclinical EDS causing the shortened latencies, as subjective
estimation of sleepiness can be distorted (Dement et al. 1978). As the differentiation
between patients and controls was more clear with VASS the use of short microsleeps to
detect increased tendency to sleep can be seen as useful.
Correction of sleep onset latencies to the first apnea/hypopnea did not increase the
sensitivity of MSLT in distinguishing between patients and controls. In this respect the
effects of VASS were observed especially in patients, which may have implications for
clinical diagnostics. According to the present study, MSLT scored by VASS could provide
a fairly sensitive objective method of measuring sleepiness.
6.2.3. Psychometric tests
In general all psychometric tests (except the FM-test) showed reasonable trends between
groups, but no significant differences were found. Corresponding results have been
obtained before, as correlations between nocturnal parameters, objective measurements of
sleepiness and subjective assessment of somnolence have been variable (Roth et al. 1980,
Roehrs et al. 1989, Manni et al. 1991, Chervin et al. 1995, Harrison and Horne 1996a,
Geisler et al. 1998).
One of the aims of the present study was to find out whether some other MSLT parameter
than LatS1RKS could be used to distinguish between apnea patients and controls. In
addition to the correlations with respiratory parameters LatS1RKS had a modest
correlation, for example, with RT-miss. However, the only correlation coefficient of
LatS1RKS that was the strongest was obtained between LatS1RKS and TST. In this
respect other latencies presented higher correlation coefficients to nocturnal and
psychometric parameters. LatHYP had the best correlation of all latencies with AHI but
other correlations, especially with nocturnal parameters, were modest. This implies that
LatHYP reveals sleep apneas well but offers no marked advantages as compared to
LatS1RKS in evaluating sleepiness.
Nocturnal sleep fragmentation is considered to cause daytime sleepiness or shortening in
MSLT latencies (Roehrs et al. 1994, Philip et al. 1994). However, no correlations have
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been found between MSLT score and nocturnal sleep disruption defined by the
conventional methods (Roth et al. 1980). In the present study the indices indicating
nocturnal sleep fragmentation had modest correlations only to LatS1VASS; the more
fragmented nocturnal sleep by awakenings or microarousals, the shorter LatS1VASS. It is
worth mentioning that arousals scored according to the ASDA (1992) criteria did not show
any correlations but arousals scored by modified rules derived from the definition of CAP
A phase (Parrino and Terzano 1996) and “Definite arousals“ from Evans (Evans 1992,
Evans 1993) had a moderate association with LatS1VASS. This further supports the idea
that paying attention to sleep microstructure would provide better parameters to
distinguish between patients and normal healthy subjects.
LatS1VASS and LatSEM both had the three highest correlation coefficients of all
latencies to nocturnal parameters. In addition, LatSEM presented the four strongest
correlations with psychometric tests and the highest total number of significant
correlations (11). On the other hand LatSEM had associations only to subjective tests
indicating sleepiness. Neither LatSEM, nor LatS1VASS had any associations to the
vigilance test or to the reaction time test. Instead LatCum30 also had 11 significant
correlations to parameters and it did show moderate associations with Vigil and
RTmiss.
As Broughton (1982) has emphasised, sleepiness may not be just a single entity. The
current data strengthens the impression that possibly several parallel parameters could
describe sleepiness better than LatS1RKS. It seems that subjective sleepiness would be
best revealed by LatSEM, while short LatS1VASS would have an association with poor
nocturnal sleep and LatCum30 would correlate well with other objective methods of
assessing sleepiness. Applying these parameters to clinical practice is so far prevented by
the fact that cut-off points for LatSEM and LatCum30 are difficult to define.
A multivariate approach to evaluate sleepiness was also proposed by Kronholm et al.
(1995). They correlated 19 variables derived from laboratory tests, psychometric tests and
nocturnal recordings to MSLT-defined sleepiness in a community sample. They found six
significant parameters to predict the outcome of MSLT. These were chronic daytime
sleepiness, body mass index, psychological distress, nocturnal motor activity, the serum
TSH level and age. The subjective persistent experience of daytime tiredness was the most
powerful predictor of the MSLT outcome.
No reasonable model to predict MSLT-defined sleepiness was found in the present study.
This is not surprising, as a trial with an even larger patient population has failed
(Guilleminault et al. 1988). One would, however, have expected logical predictors of
MSLT outcome to be found as adaptive scoring clearly increases the sensitivity of
MSLTs.
The inconsistency between different kinds of methods to assess sleepiness was also
expressed in the present study. In both the RT-test and Vigil the fastest mean reaction time
was achieved by a patient. The patient having the fastest mean reaction time in RT-test
had a LatVASS of 2 min. 31 s and in spite of this his mESS score was only 4. The slowest
mean response time was obtained by a control subject, having a LatVASS of 4 min. 44 s
and mESS of 4. The patient having the fastest mean reaction time in Vigil had LatVASS
of 6 min. 42 s with mESS score as high as 8. On the other hand quite logically, the slowest
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responses in Vigil were achieved by a patient with a LatVASS of 1 min. 49 s and mESS of
13.
6.3. BENEFITS OF VASS ON THE STUDY OF SLEEP DYNAMICS – Is VASS suitable
for the study of sleep onset dynamics?
Sleep onset is considered to consist of fluctuation between higher and lower vigilance
states. The fluctuating nature of S1 was already pointed out by Johnson (1973). With
VASS the nature of fluctuation of vigilance can be revealed. Both groups had lot of
transitions from S1VASS to wakefulness, which can mostly be considered as
physiological fluctuation between stages at sleep onset. The increased stage definition in
scorings revealed the onset of sleep to be more fragmented in the patients who presented
more transitions from S1VASS to arousal-1. This implies the state changes to be more
dramatic in patients, also causing increases in muscle activity. As the apneas disturbed the
process of falling asleep in the patient group, resulting in increased sleep onset
fragmentation and artificially prolonged conventional MSLT latencies, the respiratory
events in the controls did not significantly affect the latencies. In that way the respiratory
fluctuation in controls would be an expression of the physiological alternations at sleep
onset concomitant to the naturally occurring less disruptive EEG changes. By VASS this
separation between normal and pathological alternation of states is possible as
wakefulness related relaxed alpha activity and arousal related alpha activity are scored
separately. This is important in distinguishing between physiological state fluctuation and
sleep disturbances.
A very similar approach is provided by CAP scoring, where alternating arousal-related
phase A patterns have been divided into 3 subtypes by the morphology of EEG and by the
intensity of the arousal (Parrino et al. 2000). Subtype 1 is considered least disruptive.
Subtype 1 was found to be reduced in sleep apnea patients, whereas there was an increase
in the amount of subtype 3 phase A:s. After CPAP treatment the relationship between
subtypes was reversed and resembled the proportions in the control group. The benefit of
VASS over CAP is that it gives a more precise morphological picture of polygraphic
phenomena taking into account also the eye movements as an additional indicator of
vigilance. VASS also distinguishes, as described above, between alpha rhythm with and
without EMG-augmentation, which is not provided by CAP scoring.
In young adults 87 % of CAP A phases are separated by a mean interval below 60 s
(Terzano and Parrino 1991) the dominant interval being 15 s as is visualised in their
figure. The various A phases were not studied separately. When sleep was disturbed by
white noise 95 % of the CAP A phase intervals were found to be below 60 s and the
dominant interval shortened to 10 s. In elderly subjects 93 % of the intervals were below
60 s, the dominant interval being 10 s.
Evans studied the naturally occurring fluctuation of EEG measured vigilance consisting of
periods of higher arousal associated with a shift towards wakefulness and periods of lower
arousal with a shift towards sleep (Evans 1992, Evans 1993). In determining the dominant
interval in S1 she used a minimum of 3 s alpha burst as higher vigilance state but the
minimum length of the theta burst was not reported. A dominant interval of 16 s to S1 was
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obtained. In the present study both the patients and controls presented shorter dominant
lengths in S1VASS periodicity than those found by Evans. In addition, the dominant
interval of the patients was shorter than the dominant interval of the controls. It was
expected that shorter intervals could be obtained with the minimum epoch length of 1 s in
the present study. The period intervals are, however, surprisingly short. As the lengths of
recordings or sleep periods are not stated in Evans’ studies it cannot be postulated whether
the longer dominant intervals result from longer recording times. The dominant intervals
obtained for S2VASS were not far from the dominant interval of 50-60 s found by Evans
(1993). However, a clear difference between groups was noted in S2VASS, as half of the
intervals were below 60 s in the patient group and only one fourth in the controls. This can
be considered a reflection of the fragmentation of S2 in patients. On the other hand, since
the amount of S2 in the present study is low due to the artificial finishing of the recordings
after 15 min. of sleep one cannot draw a firm conclusion.
The dominant intervals and period intervals of the controls in the present study are close to
those described by Evans (1992, 1993) and Terzano and Parrino (1991). The results in
these three studies support the view of intrinsic physiological oscillations during sleep and
sleep onset.
In the current study a successive lengthening of dominant intervals with lowering of
vigilance was observed in the control group. The proportions of short period intervals also
diminished. This trend was not equally visible in the patients. In that way the normal
progress of the sleep process is characterised by lengthening of the period intervals with
decreasing vigilance. This observation further suggests that the progress of the
physiological oscillatory process in apnea patients might be disturbed, as the lengthening
of the periodicities is restricted. The same idea can be applied to the finding that the
dominant intervals of CAP found in controls have been seen to shorten with the increase
in the proportion of short period intervals by artificial sleep disturbance as by noise and
with ageing (Terzano and Parrino 1991). In the present study controls underwent a large
amount of stage transitions between wakefulness stages whereas transitions within sleep
were rare. Instead, patients experienced fewer transitions between wakefulness, but more
within sleep. It seems that the advance of the process of sleep onset is prevented in OSAS
as well as in artificial sleep disruption. The subjects remain in a state of drowsy
wakefulness for a longer time with a delay in entering “true sleep“.
Slow oscillation represents a basic cellular mechanism, whereas CAP and other
fluctuations are more an expression of the sleep process. As Amzica and Steriade (1998a)
have stated, at sleep onset with decreased amplitude of EEG no sleep oscillations are
present, the slow oscillation starting to have an effect on the EEG only in S1. At this state
the vertex waves become visible. The present work with Evans’ studies demonstrates
alternation of states already much earlier than S1 can be scored. In addition, oscillations in
EEG power spectra of corresponding periodicities have also been observed in restful
wakefulness (Novak et al. 1997). These oscillations have been shown to bear relationships
to changes in the functions of the autonomic nervous system that are regulated mainly
from the brainstem level. In that work it was postulated that the EEG oscillations
including the slow oscillation could also be regulated by the same structures. It is not
known whether changes in vigilance were related to the EEG oscillations. Part of the
oscillations, especially those occurring in “deep relaxation“ where theta activity was
present could be reflections of oscillations in lowered vigilance, as in the present and
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earlier studies dealing with sleep onset. As stated, slow oscillation does not begin until in
S1 with vertex waves (Amzica and Steriade 1998a). If this is true the oscillations found in
wakefulness are not necessarily produced by the same mechanisms as the slow oscillation.
It is also too early to speculate whether slow oscillation or some other mechanisms
underlie the fluctuation at sleep onset described earlier by Evans, Ogilvie and Hori or the
alternation of CAP. However, it is clear that an efficient method is required for the
research of these phenomena. According to the present study VASS seems to provide a
suitable tool.
6.4. SPECTRAL COMPARISON BETWEEN RKS AND VASS – Does VASS provide a
better basis for automatic sleep analysis than RKS?
6.4.1. Alpha and delta-theta power
As expected, the amount of alpha activity in RKS was higher in wakefulness than in light
sleep. Likewise the delta-theta power was higher in light sleep than in wakefulness.
However, the spectral differences between wakefulness and S1 were more distinct by
VASS than by RKS. Previously the power of 3 – 7 Hz band has been shown to already
increase before S1 can be scored by RKS (Badia et al. 1994). This implies that sleep states
alternate between wakefulness and S1 already before the rules of the standard scoring
system allow S1 to be scored. In the present study more theta was also found in S0RKS
than in WA and WL. On the other hand the fluctuation is assumed to continue after S1 is
scored. In the present study a considerable amount of alpha activity was found in S1RKS.
VASS revealed that the lower discriminative power of RKS can partly be explained by the
existence of sleep episodes in S0RKS and wakefulness episodes in S1RKS (Table 12 a).
In addition, both S0RKS and S1RKS had segments of low voltage activity. As
wakefulness with low-voltage activity had a different spectral outcome than wakefulness
with alpha activity, it is clear that the presence of low-voltage activities in S0RKS
diminishes the amount of alpha power. Likewise in S1RKS, low-voltage activity
diminishes the amount of theta activity. The increase of temporal resolution provided by
VASS is necessary in order to obtain a precise picture of the sleep onset process.
Although the C4 derivation has to be used in RKS scoring the highest power of the
wakefulness related alpha activity was found occipitally. In S1RKS the maximum of alpha
activity was located centro-occipitally, being higher in O2 than in C4, although this
difference was not significant. With decreasing vigilance the amount of alpha decreases in
RKS. In VASS a more complicated pattern is visible with two different wakefulness
stages. In WA alpha power was clearly occipital, whereas in WL a small amount of alpha
activity was diffusely distributed. In S1VASS the alpha power was centro-occipitally
located and although not significant, the power was higher in C4 than in O2. In RKS the
assumption is that alpha activity is a characteristic of wakefulness. However, there was
more alpha in S1VASS than in WL. Consequently, in VASS alpha activity may either
decrease or increase with lowering of vigilance. The sleep-related alpha activity is not a
clearly occipital rhythm but it is also abundant centrally. This implies a slight
topographical change in alpha activity along with VASS stages. A significant spreading in
alpha activity during drowsiness has been observed before (Cantero et al. 1999). When C4
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is used in RKS it is evident that drowsiness and sleep related alpha activity may also be
erroneously taken as a sign of wakefulness.
The frontal dominance of delta-theta power in S0RKS, WA and WL is obviously
explained by the presence of fast eye movements. As rapid eye movements diminished
with decreasing vigilance, as in S1RKS and S1VASS, the maximum power of the deltatheta band was found to move more centrally with no significant difference, however,
between the frontal and central derivations.
To summarise, if sleep-wake differentiation is to be made by spectral means, a successful
result can be obtained using only 2 power bands. In general the best spectral separation
can be achieved using occipital alpha activity and central delta-theta activity. Both
derivations can be considered significant.
The amount of alpha activity cannot be used in distinguishing between wakefulness and
sleep in poor alpha producers. As the discriminative power of delta-theta activity in the
present study was found to be satisfactory, it could be postulated that in these situations
the separation between wakefulness and sleep by spectral means could be made by the
power of the central delta-theta activity. However, in one earlier study only minor spectral
changes were found for low-alpha subjects between wakefulness and S1 (Johnson et al.
1968). In that study four subjects with low alpha activity were studied. It was concluded
that it is difficult to distinguish wakefulness, S1 and SREM by spectral means in low
alpha subjects. A similar finding was obtained in the present study as no significant
differences in delta-theta activity between C4WA-C4S1VASS or C4WL-C4S1VASS were
found in the subject with poor alpha.
6.4.2. Differences in peak frequencies between RKS and VASS
With the aid of the peak frequency VASS stages could be divided into two; stages with
prominent alpha peaks and stages with the peak in the delta-theta range. As expected, the
peak powers of low-amplitude VASS stages were quite low. It was surprising that the
peak frequency of C4S0RKS did not differ significantly from C4S1RKS although the
medians were completely different, the former being in the alpha range and the latter in
the delta-theta range. This can be explained by the wide dispersion of the peak frequencies
caused by overlap in the EEG patterns related to stage scoring with long, fixed epochs. In
contrast, as the VASS stages were scored with higher temporal resolution, the peak
frequencies of VASS wakefulness alpha stages differed clearly from low-voltage stages
and S1VASS.
6.4.3. VASS as a basis for automatic analysis
In most automatic analysis methods the assumption is that the segments analysed are
stationary. In sleep analysis one epoch should therefore consist only of one stage. The
present study confirmed that RKS stages usually consist of several VASS stages (Table 12
a). This was especially true with patients who had fragmented sleep. As can be seen in
Figure 16, the spectral parameters do show different trends along with VASS stages. No
completely similar spectral indices in various stages were observed. Due to the fixed
epochs, the spectra made of RKS were averages of spectra from different VASS stages.
The long RKS epochs consist of shorter segments with different patterns having alpha,
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delta and low-voltage EEG. Thus the demand for stationarity within the segment analysed
is not fulfilled. When the electrophysiologically different segments are analysed
separately, more correct results are obtained.
The more detailed stage division is also assumed to provide a better basis for automatic
sleep analysis. This would be a benefit not only with automatic sleep scoring but also in
pattern recognition. For instance, arousals vary substantially by morphology. One reason
for the generally poor man-machine agreement in arousal detection (Huupponen et
al.1996) might be that only one computer algorithm is used for the detection of all kinds of
arousals. As arousals with different morphology are visually separated by VASS the
computer algorithms can be modified to cover all the different patterns.
6.5. FREQUENCY BAND POWER CHARACTERISTICS OF VASS STAGES
6.5.1. Topographical differences of the peak frequencies in VASS
Although the differences between peak frequencies in VASS alpha stages did not differ
significantly (with the exception in C4 between WA and SAF) a decreasing trend along
with stages was noted with a fairly slow central alpha peak in SAF. This is in concordance
with previous findings where topographical change in the dominance of alpha power from
occipital to central location with slower frequency during lowered vigilance has been
observed (Broughton and Hasan 1996).
6.5.2. Band power differences between adjacent VASS stages
In general theta power was abundant in stages with high alpha activity. It may happen that
adjacent power bands overlap and part of the alpha activity leaks into the theta power
band. The large amount of sigma power in high alpha stages may also result from
overlapping between alpha and sigma bands. Due to this overlapping it would perhaps be
preferable not to use continuous division into frequency bands but to leave small gaps
between adjacent power bands. Continuous division was, however, applied in the present
study since one purpose was to compare results to former spectral studies where usually
continuous band divisions have been used.
WA versus SA
Visually WA and SA were differentiated by the appearance of SEMs. This stage division
by eye movements had only minor effects on the EEG spectra. That theta and delta powers
were higher frontally in WA than in SA is explained by the effect of fast eye movements
on power bands and reflects the difference in the visual scoring criteria of these stages. A
very small decrease was seen in beta activity between WA and SA, and frontally this
decrease was significant. As beta activity has been found to decrease with decreasing
vigilance (Hori 1985, Alloway et al. 1999) this may be a minor sign of lowering of
vigilance. Thus SA would present a lower vigilance state than WA. The frontal difference
in sigma power between stages is slight and remains unexplained.
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WAF versus SAF
Visual separation between WAF and SAF was based on the presence of SEMs. One would
have expected that theta and delta powers would be higher frontally in WAF than in SAF
due to fast eye movements, but this was not the case. It seems that eye movements are
more alike between WAF and SAF than between WA and SA. Kojima et al. (1981)
studied connections between eye movements and alpha activity. They observed fast eye
movements to decrease and small slow eye movements to increase in Resting stage II,
where alpha activity was diffuse and slower than in wakefulness. Resting stage II
resembles WAF by frequency and topography of alpha activity. This supports the view
that eye movements could be slower in WAF than in WA. This difference was not evident
by visual judgement. In the beginning of the study the hypothesis was that WAF
represents a lower vigilance state than WA. The finding that frontal delta power of WAF
resembles SAF further supports the idea.
In addition, signs of lowered vigilance were found in spectra derived from SAF but not
from WAF. First, there was a central topographical dominance of alpha power in SAF
which was not present in WAF. Secondly, the amount of theta activity was significantly
higher centrally in SAF than in WAF. As alpha activity has been seen to have centrofrontal dominance during lowered vigilance (Broughton and Hasan 1995) and the amount
of theta activity can be considered as a sign of lowered vigilance, both these findings
support the view that WAF and SAF reflect different states of vigilance.
WL versus DL
The spectral differences between WL and DL were quite modest. The amounts of theta
and delta activities were higher frontally in WL than in DL due to fast eye movements. In
addition the theta power was significantly higher occipitally in DL than in WL. This may
reflect the lower vigilance state. One former study shows neither regional changes nor
sleep state changes for theta activity (Buchsbaum et al. 1982). Hori (1985) found the
power of the occipital theta activity to increase more slowly than in the other regions
during sleep onset. Tanaka et al. (1997) observed theta activity to extend from central to
temporal regions as a function of stage. Wright et al. (1995) noted theta activity to be high
at central brain sites. In the O2 derivation theta power was found to be higher in last
minute of S1 as compared to the first minute of S1, but no difference was observed
between the first minute of wakefulness and the last minute of wakefulness. The occipital
location for theta activity changes before S1 seems not to be typical as expression of
lowered vigilance. However, in the work of Badia et al. (1994) an occipital increase of
theta activity from wakefulness to S1 is visible. Despite the visual scoring rules, where the
differentiation between WL and DL was based only on the appearance of SEMs, a small
but logical increase in theta activity was found in DL, although its location was
unexpected.
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S1VASS versus S2VASS
Some spectral differences were observed between S1VASS and S2VASS. The amounts of
alpha, theta, delta and sigma activities were higher in S2VASS than in S1VASS in almost
all derivations. That the frontal delta activity of S1VASS and S2VASS did not differ much
from the activity of the other stages could be explained by the high prevalence of eye
movements in frontal channels, which increases the frontal power of the delta band in
most of the stages. However, SEMs are also supposed to increase the frontal delta activity
in DL. The amount of delta activity was higher in S2VASS than in DL, which can be
considered a true indication of increased amount of slow EEG activity in S2VASS as slow
eye movements generally diminish in S2 (Ogilvie et al. 1988).
Aa2 versus S2VASS and VASS alpha stages
Signs of higher vigilance state are present in Aa2 as compared to S2VASS. In addition the
spectra of Aa2 differ from alpha wakefulness stages, as alpha power was lower in WAF
and SAF than in Aa2. Through the different topographical distribution of alpha activity
Aa2 also differs from WA + SA. This reinforces the impression that normal physiological
stage fluctuation at sleep onset can and should be differentiated from more dramatic sleep
fragmentation, which is reflected by EEG arousals with the increase in submental EMG
activity.
EMGW and MTW
In general all band powers were quite high in these stages and the dispersions were wide.
This explains why some comparisons between these two stages and other stages did not
reach statistical significance. As the total power was high in these stages it would surely
affect the spectra of other stages if EMGW and MTW were not scored separately. In
general, all former FFT studies have been performed in artifact-free epochs. This means
that EEG segments with EMG augmentations or movements, which are usually considered
as artifacts, have been removed before the spectra were calculated. As these segments are
separated into their own entities in VASS their quantities and properties can also be
analysed. These events are usually associated with changes in the autonomic nervous
system and therefore also of interest in the study of sleep dynamics (Alihanka 1982).
6.5.3. Band power characteristics along with VASS stages
In most stages the maximum of theta and delta powers were frontal or fronto-central. Due
to the huge effect of the eye movements on these bands the differences between stages can
be better observed by the central and occipital derivations. The band powers in C4 and O2
of VASS stages are presented schematically in Figure 24.
Alpha power
Both occipital and central alpha activities showed a “W“ shape along with VASS stages.
The powers in WA + SA differed significantly from other stages, as also did their
distributions. Significant central increase in alpha power was observed in WAF and SAF,
whereas powers were low in low-amplitude stages and S1VASS. This “W“ shape trend in
alpha power has not been described before. It is visualised due to more detailed stage
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division, where low-amplitude stages are scored separately. Alpha activity in both
derivations showed an increase from S1VASS to S2VASS and a more marked increase
further to Aa2.
Previously decreases in alpha activity with decreased vigilance have been reported (Badia
et al. 1994, Wright et al. 1995, Alloway et al. 1999). This partly corresponds to the
decreasing trend in alpha activity found in the present study. An additional increase in
alpha power at sleep onset (defined by response failure) has also been described (Ogilvie
et al. 1991, Ogilvie and Simons 1992). As response failures have been connected to real,
behavioral sleep, there is consistency between these studies and the current finding with
increasing alpha activity in S2VASS. In VASS the increase in alpha activity between
S1VASS and S2VASS was small. One could, however, conclude that alpha activity is
increased in S2VASS, especially as arousals were scored separately and did not affect the
spectra.
Diffuse or anterior slow alpha is claimed to be a sign of drowsiness (Broughton and Hasan
1995). This is in agreement with the present work where SAF was the only stage where
significant central topographical dominance for alpha activity was observed.
In the present work no significant topographical alpha power differences between central
and occipital derivations were obtained in S1VASS or S2VASS. Previously at least two
alpha patterns during sleep have been described, where the dominance of alpha power is
central or frontal but not occipital (Moldofsky 1990, Scheuler et al. 1990). Frontal
increase in alpha activity has been reported in Hori’s stage 7 (Tanaka et al.1997). One
previous finding is consistent with the current study describing the alpha power in S2 to be
largest at posterior brain sites (Wright et al. 1995). In both these studies early S2 was
examined. It would be of interest to see whether the dominance of alpha activity is moved
to more central derivations again with deepening of sleep. In Fig. 4 of the study by Hori
(1985) the alpha power was highest in Pz 10 min. after sleep onset, whereas a few minutes
later a central dominance appeared. Another group described alpha power to move into
more anterior positions in S3 and S4 (Buchsbaum et al. 1982). In the present study with
MSLTs no slow wave sleep was found at all.
Theta power
Theta power also showed a “W“ shape along with VASS stages in both derivations. It was
quite high in WA and SA decreasing significantly in WL. The increase in WAF was
significant occipitally and the increase between WAF and SAF was significant only
centrally. The decrease in DL was significant in both derivations, as was the increase in
S1VASS and further in S2VASS. There was no difference in theta activity between
S2VASS and Aa2. Even if the theta power was high in WA and SA, it was significantly
higher occipitally in S2VASS. The power maximum was central in S1VASS and centrooccipital in S2VASS.
In previous studies increase in theta activity together with decreased vigilance has been
noted (Hori 1985, Ogilvie et al. 1991, Badia et al. 1994, Ogilvie and Simons 1994, Wright
et al.1995). Slower and smaller increase has previously been found occipitally (Hori
1985). Increase from S1 to S2 was found to be non-significant in one study (Wright et al.
1995). In contrast to these works no theta power changes were found during sleep onset
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period (Alloway et al. 1999) or between different sleep states or derivations (Buchsbaum
et al. 1982). Tanaka et al. (1997) did not find significant regional changes of theta activity
in Hori’s stages 1-9.
In the present study theta power was significantly higher in C4 than in O2 in all VASS
stages except in S2VASS or stages with arousals/movements. The wider distribution of
the theta activity in S2VASS is an interesting, new finding. Its meaning remains
unexplained.
Delta power
Delta activity showed first non-significant changes along with VASS stages with a slight
decreasing trend in both derivations. The increase was already significant and clear in
S1VASS and even more marked in S2VASS. Power was higher centrally than occipitally
in all stages. Although the difference between S2VASS and Aa2 seems to be clear, it was
not statistically significant due to the large dispersion in powers in Aa2.
Topographically the findings in the present study corroborate earlier studies in which a
central dominance for delta activity has been found (Buchsbaum et al. 1982, Hori 1985,
Tanaka et al. 1997). The present findings are also consistent with previous studies, where
delta power has been seen to increase during decreasing vigilance (Buchsbaum et al. 1982,
Hori 1985, Ogilvie and Simons 1994, Alloway et al. 1999). In one study delta increase
reached statistical significance only at sleep onset defined by response failure (Ogilvie et
al. 1991). In the study by Hori (1985) no clear delta power increase was visualised at the
onset of S1. The occipital and central powers were close to each other with a higher
increase centrally a few minutes after S1 onset. In the present study a significant delta
power increase in both derivations was already seen in S1VASS with more marked
increase centrally in S2VASS.
As described above, practically all previous studies demonstrate that the delta power does
not increase until true sleep (S2) begins. In that way delta activity seems to be closely
related to the process of falling asleep and not to pre-sleep states. In the present study a
significant central delta power increase was already found in S1VASS. By visual
judgement there is not usually noticeable delta activity in S1. It seems that the increase in
spectral delta power at least in part reflects the periodic appearance of vertex waves in
S1VASS. In S2VASS the increase in delta power also consists of periodic spindles and
KCs. These periodicities of the phasic events are assumed to be manifestations of the slow
oscillation already increasing the delta power in S1VASS in the present study.
The effect of periodic phasic events on EEG spectra is enabled by the segmentation
method used in VASS. As the EEG segments last as long as the signal is stationary, the
increase in temporal resolution can lead to even longer EEG segments than the
conventional scoring rules provide for. In the present study the longest segment scored
was in S2VASS, being 12 min. 35 s. It is evident that phasic events with slow periodicities
do have an effect on spectra when such long stretches are studied. With short, quasistationary segments it may happen that there is only one phasic event within an epoch and
its periodicity cannot influence the spectra. As the spindles usually recur with an interval
of 4 s (Kubicki et al. 1986, Spieweg et al. 1992, Evans and Richardson 1995, Achermann
and Borbély 1997) the commonly used segment length of 5 s can be considered far too
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short. That over 40 % of the S1VASS + DL segments in the present study lasted over 6 s
confirms the need of stationary segments for spectral analysis of EEG instead of the use of
artificially split short segments.
Sigma power
Fronto-central maximum was found in WL and DL. In WA and SA the maximum was
centro-occipital. A small “W“ shape was again seen in sigma power along with VASS
stages. The power was high in alpha stages, but a significant decrease in both derivations
was observed in WL with significant increase in WAF. The decrease between SAF and
DL was also significant in both derivations, as were the increases between S1VASS and
S2VASS. The maximum of sigma activity was central in WAF, SAF, S1VASS and
S2VASS.
As sleep spindles last only approximately 0.5 – 2 s they cannot be expected to have a great
effect on sigma activity. Regardless of that, a significant increase was also seen in the
present study in S2VASS. Sigma activities in DL and S1VASS did not differ from each
other, which means that sleep spindles were not likely to be present in S1VASS. As the
visual scoring of S2VASS was started from the first spindle or KC, the result confirms the
validity of visual spindle observation.
In one study an increase in sigma activity, significant at sleep onset, has been observed
(Ogilvie et al. 1991). Before Hori (1985) had found a decrease in sigma activity soon after
S1 with a rapid increase in a few minutes. Occipital power was lower. In Hori’s stages 1-5
no noteworthy changes in the topography of sigma activity were observed (Tanaka et al.
1997). After stage 6 a dominant sigma focus appeared parietally with a sharp increase in
stage 8, the stage with vertex waves and incomplete spindles. The results are in agreement
with the present study.
Beta power
A slight “W“ shape was again seen in beta power along with VASS stages in both
derivations. High powers were obtained in high alpha stages, with a marked decrease in
WL. The increase in WAF was also significant. The decrease from SAF to DL was
significant. The small increases through S1VASS to S2VASS were not significant. Instead
in Aa2 beta power increased markedly as compared to S2VASS in both derivations. The
decreasing trend WA – DL was also significant in both derivations. Power maximum was
centro-occipital in WA and SA, central in WAF, SAF, and S2VASS and fronto-central in
WL, DL and S1VASS.
A decreasing trend in beta activity with decreasing vigilance has been observed before
(Hori 1985, Alloway et al. 1999). Wright et al. (1995) found a decrease in beta activity
from the first minute of wakefulness as compared to the last minute of wakefulness and a
non-significant increase from the first minute of S1 to the last minute of S1. In two works
a decreasing tendency with sharp and significant increase at sleep onset was found
(Ogilvie et al. 1991, Ogilvie and Simons 1994). Power seemed to be higher centrally than
occipitally in one study dealing with topography (Hori 1985). In the present study the
decrease in beta activity was clearer occipitally, which is in agreement with previous
results (Wright et al. 1995).
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In VASS all arousals were scored separately. Since beta activity in Aa2 was markedly
increased, one can assume that beta power increases in S1VASS and S2VASS would have
been higher without visual separation of arousals.
6.6. VALIDITY OF STAGE DIVISION BY MORPHOLOGY – Do VASS stages present
spectral differences specific to different vigilance states?
It has been suggested that SEMs are perhaps the most consistent indicators of drowsiness
(Kojima et al. 1981, Santamaria and Chiappa 1987, Hasan et al. 1993). The focus of
interest is whether SEMs could be used as a simple but reliable indicator of sleepiness. For
this reason the similar EEG patterns with different kinds of eye movements have been
separated in VASS.
In general the morphologically defined VASS stages did show significant spectral
differences, implying different vigilance states. In fact the only marked exception was the
spectra derived from WA and SA. A small but significant decrease was seen in frontal
beta activity between WA and SA. In addition the frontal peak frequency in WA was in
the delta-theta range, whereas in SA the frontal peak frequency was in the alpha range.
Although no alpha power differences were found between WA and SA, the frontal alpha
peak in SA suggests that alpha activity might have spread more anteriorly. This could
mean that SA represents a lower vigilance state as widening distribution of alpha activity
has been observed with lowered vigilance (Santamaria and Chiappa 1987). Regarding the
amount of alpha activity WA and SA differed significantly from all other stages and the
distribution of alpha activity was different between these stages and WAF. As central
spreading of alpha activity can be considered a sign of lowered vigilance, WAF would
present a lower vigilance state than WA or SA.
WAF and SAF could be distinguished from each other by the amount of central theta
power. In addition, there was a nonsignificant difference in the central peak frequency
between the stages. The spectral indices indicate that WAF represents a higher vigilance
state than SAF. The visual differentiation between WAF and SAF by SEMs can therefore
be considered useful. They could both be differentiated from low-amplitude stages WL
and DL.
None of the former scoring systems has separated low-voltage stages by eye movements.
Low-amplitude stages are not taken into account by RKS, where low-amplitude activities
are scored into S0RKS or S1RKS. In Hori’s 9-stage system only one stage for lowamplitude activity is defined. Thus Hori’s low voltage stage, stage 4, is assumed to consist
of both WL and DL. In the present study the spectral differences were minor between WL
and DL. Previously the response times have been shown to lengthen along with Hori’s
stages 1-3 with alpha activity and further to stage 4, with low voltage activity (Hori et al.
1991, Hori et al. 1994). Thus low voltage activity can be considered a lower vigilance
state than alpha stages. On the other hand low voltage activity with fast eye movements
cannot be considered a marker of lowered vigilance. Although spectral differences
between WL and DL were small, it seems obvious that a state with fast eye movements
and one with SEMs represent two different vigilance states. According to Hori’s reaction
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time test results it can also be postulated that DL represents a lower vigilance state than all
VASS alpha stages.
WL and DL both could in general be differentiated from S1VASS, which presented
spectral indices typical for a lower vigilance state. In the present work S2VASS presented
spectral signs of the lowest vigilance state.
As described, the VASS stages can be differentiated from each other by FFT. Their
relations to each other as a function of lowered vigilance can also be mostly defined. One
problem remained with VASS alpha stages WA and SA, where the spectra strongly
resembled each other.
In the present study LatSEM was defined by the first appearance of SEMs. The stages
containing SEMs are SA, SAF, DL and sleep stages but it is not known which stages in
general appeared first. All subjects had short latencies to the first SEMs with a clear
difference between the patient and the control groups. In the patients SEMs appeared on
average in less than a minute after lights off, while the control subjects’ LatSEM was
about 3 min. However, in the control subjects the time between the first SEM and sleep
onset was significantly longer than in the patients. As the control subjects did not report
sleepiness at the beginning of the test protocol it is not clear if SEMs can be considered
strong proof of somnolence. There could be also other mechanisms than somnolence
producing rolling eye movements in subjects lying still with their eyes closed.
As WAF and SAF did present spectral signs of lowered vigilance, the question arises
whether posterior alpha activity with SEMs (SA) could be taken as a marker of lowered
vigilance at all, as the possible lowered vigilance is not better reflected in the EEG spectra.
This would in part explain the early LatSEM and long time period from LatSEM to sleep
onset in the control subjects.
On the other hand LatSEM did show a great deal of marked correlations to nocturnal and
psychometric parameters. It has also been stated that the occurrence of SEMs is a reliable
sign that the sleep onset period has been entered and that sleep is likely to follow very
shortly (Ogilvie et al. 1988). It is possible that in the present study the control subjects had
subclinical EDS with early SEMs as the only sign of it. The major difference in the
quantity of sleepiness between patients and controls could then have been the better
capability of the controls to resist the process of falling asleep in MSLT.
SEMs have been proven to be reliable indicators of sleepiness in subjects who are
expected to be awake with eyes open (Åkerstedt and Folkard 1994). The current data
suggests that the issue might not be as simple with closed eyes. To the best of the author’s
knowledge although SEMs have been studied a lot, a study where eye movements, the
topography of alpha activity and reaction times have all been combined has not been
conducted. It would be of interest to find out the psychophysiological differences between
WA and SA and WAF. In the future the division into stages by SEMs must be reevaluated.
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6.7. FFT AS QUALITY CONTROL OF VISUAL SCORING
FFT results can be used as quality control of visual stage scoring. As the alpha power was
markedly higher in WA and SA than in the low-voltage stages (WL, DL) the division into
stages can be based on the amount of alpha activity.
Topographical differences in alpha activity can also be used in differentiating vigilance
states visually. This is supported by the results of occipital topographical maxima of WA
and SA in contrast to central dominance in SAF and wide topographical distribution in
WAF.
The quantity of delta and theta activities was lower in DL than in S1VASS. This means
that visual differentiation between DL and S1VASS was also successful.
The peak frequencies in VASS stages were as expected. A clear alpha peak was found in
all alpha stages. Low peaks were found in stages with flat EEG.
6.8. VASS AS A SCORING SYSTEM
6.8.1. Repeatability of VASS
In the present study 9 vigilance stages together with 9 stages with arousals, EMGaugmentation and movements were used. As the segment boundaries change adaptively,
the repeatability of scorings can be considered satisfactory. This is certain taking into
account that scorings consisted mostly of alternation between different wakefulness stages
or between wakefulness and light sleep. As is well known, the inter-rater agreements in
RKS are low particularly for these stages. With sleep disorders the inter-rater differences
in RKS become even more pronounced. With VASS the repeatability scores for patients
and controls did not show any differences. This supports the use of an increased number
of stages in scoring. The polygraphic patterns, including EEG, were quite similar in
patients and controls. As in RKS one epoch may consist of as many as three different
vigilance states with only small differences in duration, a common difficulty in scoring is
to determine which state is of longest duration. In VASS the recording is scored
adaptively in segments divided according to the morphology of the signals. Therefore
small differences in the estimation of state durations do not affect the scoring result of the
whole 30 s epoch.
6.8.2. VASS in clinical practice
In a recent study MSLT as a measure of sleepiness was harshly criticised (Johns 2000).
The use of subjective estimation (ESS) was proposed instead of MSLT. In the present
study mESS did not distinguish between patients and controls. As the advantages of
MSLT over other methods measuring sleepiness are highly appreciated (Dement et al.
1978, Matousek and Petersén 1983) and as MSLT is widely used, efforts could be directed
to the improvement of sensitivity in scorings.
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The tendency to sleep rather than the amount or maintenance of sleep is the variable of
interest in the MSLT. VASS reveals that short microsleeps appear before the first epoch of
S1, which has been chosen as the marker of sleep onset. However, the duration of the
shortest sleep duration of clinical significance is unclear as no reasonable model to predict
MSLT-defined sleepiness was found in the present study. Instead of defining latencies to
the first respiratory event, as some laboratories do, SOL could be defined to the first sleep
episode but the problem with determination of a sufficiently long sleep episode remains.
On the other hand, as not only shorter latencies but also many other different parameters
can be obtained by VASS it is too early to propose the use of a certain VASS parameter.
The stages were based on the present knowledge about sleep physiology and in general
they presented different spectra. However, the issue with SEMs as indicator of sleepiness
with closed eyes turned to be more complicated than was expected, and VASS cannot be
applied to MSLT scoring at once. At first the psychophysiological meaning of stages with
SEMs should be studied. Instead, VASS could already be applied with existing stages to
MWT, as SEMs with open eyes are clearly a sign of lowered vigilance. The determination
of the adequate segment length remains still unsolved and further investigations are
needed.
One general problem in the evaluation of night recordings of apnea patients is that some
apneas occur during epochs that have to be classified as wakefulness by RKS. This leads
to incorrect apnea/hypopnea indices, as respiratory events during wakefulness are not
usually taken into account in index calculations. This problem can be overcome with Tsleep scoring. In T-sleep scoring the time with distorted sleep consisting of repetitive
arousals and short sleep episodes is scored into one block (McGregor et al. 1992). The
problem with T-sleep scoring is, however, that it loses sleep microstructure. The problem
of respiratory events during RKS wakefulness epochs can also be overcome by VASS,
which reveals the short sleep episodes concomitant to apneas by visualising the sleep
microstructure.
6.8.3. Disadvantages of VASS
As such VASS is quite time-consuming compared to RKS. It is also probably more time
consuming than CAP scoring. On the other hand, as also very long epochs are obtained
with VASS, the scoring of nocturnal polygraphic recordings is not supposed to last as long
as the scoring of MSLTs.
Since the significance of SEMs in determining lowered vigilance seems to be more
complicated than was expected, psychophysiological studies are needed before stages with
SEMs and alpha activity can be used as a true sign of lowered vigilance with closed eyes.
6.8.4. Advantages of VASS
Recordings were quite easy to score with VASS stages and the repeatability was also
satisfactory. At present brief oscillations with subtle state shifts are of interest. VASS
enables a detailed study of these phenomena. The present study suggests that these
oscillations might be disturbed in sleep disorders. Such studies have also been performed
with CAP scoring. Being practical and well documented CAP scoring provides an
excellent tool for both clinical and scientific work. VASS, however, provides more precise
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information for the description of vigilance with more detailed stage division and is
particularly suitable for scientific work. For example, in insomnia the amounts and ratios
of different wakefulness and drowsiness stages could be of interest.
The stages can be changed according to the main interests. Probably for whole night sleep
recordings the number of stages would have to be re-evaluated. With apnea patients
different wakefulness stages can easily be combined, as the stage shifts are quite clear,
whereas in vigilance studies more stages are needed.
In psychometric studies RKS can give misleading results (Morrell 1966, Conradt et al.
1999). This is explained by the fact that discrete epochs are a mixture of several vigilance
states. Therefore with fixed epochs it is not always clear in which state the stimulus was
presented. With VASS this problem can be overcome.
VASS can also improve the potential of computer analysis as stationary segments are
provided. EMG augmentations and movements can also be studied since they are not
considered to be artifacts.
6.8.5. Position of VASS in sleep studies
RKS has been used in sleep studies for more than 30 years. With increasing knowledge of
the sleep process and improved automatic methods, better models to describe sleep quality
are needed. The present work claims that VASS provides a more physiological model of
the sleep process than RKS. VASS can be seen as an evolution of RKS made possible by
new equipment. This evolution is in total agreement with the spirit of RKS stating that:
“the methods and concepts should be revised in accordance with the development of new
methods and knowledge“ (Rechtschaffen and Kales 1968).
The main intention of the present study was not to create a scoring method to replace RKS
in the interpretation of MSLTs. The main objective was to examine the effects and
suitability of a more subtle scoring method on the study of sleep onset and sleep
dynamics. However, VASS proved to be more sensitive in distinguishing between patients
and controls from the clinical point of view.
Concerning the study of the sleep process VASS was more sensitive in revealing short
sleep spells and sleep fragmentation than RKS. In that way VASS allows the study of
sleep dynamics exposing sleep microstructure. CAP scoring has also provided clear
advantages over RKS, describing sleep microstructure more precisely, revealing changes
in sleep parameters, which have been undetectable by RKS (Terzano and Parrino 1993,
Terzano et al. 1996). But if CAP scoring is not begun until S1 is scored by RKS, the
fluctuations at sleep onset go unnoticed. Since the different kinds of wakefulness and
drowsiness are differentiated by VASS it allows a detailed study of the fluctuating
phenomena including sleep onset. Compared to Hori’s well-designed 9-stage system the
most important advantages of VASS are the adaptive approach in scoring, separation
between similar EEG patterns by eye movements and distinction between different kind of
alpha patterns. In Evans’ studies the state boundaries changed adaptively, but the main
interest was to study the fluctuation between two different type of patterns. In VASS no
pre-determination of stages is needed, as the scoring proceeds concomitant to
morphological changes in polygraphic patterns. This enables the analysis of the recordings
120
with open eyes, promoting the discovery of new patterns instead of looking for changes in
pre-determined categories.
In this present work new sleep stages were defined whenever a new pattern of
combinations in EEG, EOG and EMG was observed. Special attention was paid to the
SEMs and the topography of alpha activity. As temporal resolution is also increased by
adaptive segmentation the advantages of VASS can be foreseen. The VASS stages
presented in this study are not meant to be definitive. At the beginning it is necessary to
define a large number of stage categories according to their morphology. With
accumulating knowledge of their physiological basis it will be possible to combine some
stages and define new entities. For this basic physiological, clinical and psychometric
studies will be required.
121
7. CONCLUSIONS
The following conclusions can be drawn from the results of the current investigation:
1. The new visual scoring method (VASS) increases temporal resolution of scorings
compared to the standardised scoring system (RKS). VASS also reveals slight vigilance
state fluctuations that remain undetected by the conventional method.
2. VASS is sensitive in detecting brief vigilance fluctuations in MSLTs. New MSLT
parameters defined by VASS stages show marked correlations to nocturnal and
psychometric parameters. However, the attempt to develop a superior parameter which
could reliably distinguish between sleepy and non-sleepy subjects was not successful.
3. VASS with increased temporal resolution and stage specificity enables a detailed study
of sleep dynamics. The period intervals found in the controls seem to shorten in sleep
apnea. The dominant intervals also changed in the patients. In addition with VASS the
distinction between wakefulness related relaxed alpha activity and arousal related alpha
activity is possible. This is important in distinguishing between physiological state
fluctuation and sleep disturbances.
4. Spectra derived from wakefulness and light sleep differed more in VASS than in RKS.
An increase in spectral stage specificity through VASS can facilitate the further
development of computerized methods to expose the sleep process automatically.
5. In the present study the stages were defined only by the morphology of the EEG, EOG
and EMG. The stage division seems to be appropriate, as the stages could be differentiated
from each other by spectral means. The only exception was the differentiation between
alpha stages WA and SA, which was visually performed by slow eye movements. As there
were no major spectral differences it is not clear whether these two stages represent true
differences in vigilance state.
122
8. SUMMARY
Sleep EEG has been categorised into sleep stages by standardised scoring rules since
1968. The rules were assessed by the Committee led by Allan Rechtschaffen and Anthony
Kales. The standardised scoring system, often called “the manual of Rechtschaffen and
Kales“ (RKS) has become practically the only method of visual sleep analysis. It is
characterised by fixed, long epochs, a discrete number of sleep stages and ignorance of
EEG topography.
In order to overcome these limitations a visual adaptive scoring system (VASS) was
developed. Epochs of variable length and more stage categories than in the standard
system are used. The purpose is to have as electrophysiologically stationary epochs as
possible.
In the present study VASS was applied to the analysis of the multiple sleep latency tests
(MSLT) of 17 subjects. Ten subjects had a clinical history of obstructive sleep apnea
syndrome and an apnea-hypopnoea index (AHI) of > 10/h in a preceding polygraphic
whole-night recording. In addition 7 healthy subjects with no history of EDS or sleep
complaints and AHI < 10 were included. None of the patients or controls suffered from
any other major disease and they used no hypnotics or other medication affecting the
central nervous system. The mean age of the patients was 50.7 (range 35-63) and the
control subjects 51.9 (25-77). Four patients and four control subjects were female.
Each MSLT consisted of four naps. The naps were terminated 20 min. after lights out if
there was no sleep at all. If the subject fell asleep, the recording was continued for 15
minutes from the first S1 epoch. Frontal, central and occipital EEG derivations were
recorded. In addition two channels of eye movements (EOG), submental muscle tonus
and respiratory variables were measured.
Each nap was scored by RKS and VASS and the mean sleep latencies of the MSLTs were
determined. The VASS stages used were 3 categories of clear wakefulness, 2 categories
of drowsy wakefulness with slow eye movements, 4 types of movements and EMG
augmentations, 2 categories of light sleep (Drowsy low and S1VASS), S2VASS,
REMVASS and several types of arousals. Electrophysiological stage changes shorter than
1 s were not separately scored.
Quality of life, subjective sleepiness and performance were measured by questionnaires
and psychometric tests. Spectral analysis by FFT was made from stages scored both by
RKS and VASS.
VASS revealed that sleep onset consists of fluctuations between wakefulness and sleep.
This fluctuation is already visible before sleep can be scored by the conventional method.
VASS provides more precise information on sleep staging, as eye movements and
topography are also taken into account. The nine VASS stages in general can be
differentiated from each other by spectral means. RKS stages were found to consist of
several different VASS stages. Therefore VASS can provide a better reference for the
validation of automatic sleep analysis.
123
The MSLT latencies scored by VASS were in general shorter than by RKS, especially in
the patient group. It seems that the sensitivity of MSLT can be improved by VASS.
However, in clinical practice the use of VASS is not unproblematic. Although VASS
scored latencies presented abundant correlations to psychometric tests, no model to
predict sleepiness could be derived. Therefore no single optimal parameter to be used in
clinical practice was found.
The greatest value of VASS is that it proved to be a sensitive method to examine sleep
dynamics both in healthy subjects and in sleep apnea patients. If it is assumed that sleep
disorders are disturbances of the sleep process, then VASS provides an efficient tool for
scientific purposes.
124
ACKNOWLEDGEMENTS
This study was carried out at the Department of Clinical Neurophysiology, Tampere
University Hospital.
First I want to express my deepest gratitude to Docent Joel Hasan, my supervisor, for
introducing me the fascinating field of sleep physiology. I am grateful for his continuous
guidance and encouraging support during the study.
I thank Docent Veikko Häkkinen and Alpo Värri, Dr. Tech., for their valuable comments
on the work.
I am very grateful to the official reviewers of my thesis, Docent Ilkka Lehtinen and
Docent Uolevi Tolonen. I truly appreciate their scientific experience, constructive
proposals and positive criticism during the process of reviewing.
I owe sincere thanks to Antti Saastamoinen, M.Sc., and Jussi Virkkala, M.Sc., for
developing necessary computer programs and for much advice. I also thank Flaga Inc. and
Fuchs Medical Oy, especially Helgi Kristbjarnarson, M.D., Ph.D., Gudmundur
Saevarsson, C.Sc., and Pertti Jalasvirta, CEO, for providing me with specific analysis
modules. Mikko Oksanen, Student of Technology, did part of the computer analysis. Lauri
Parkkinen, M.A., did a great job in supervising the physiological tests and by helping me
with the analysis of the tests.
I wish to thank all nurses in the Video EEG who were involved with recordings in this
study. My special thanks are due to Manuela Dill, who managed patients and organised all
research routines. I am grateful to all the volunteers for participating in the timeconsuming study. I also thank all my colleagues and co-workers at the Department of the
Clinical Neurophysiology especially Mirja Tenhunen-Eskelinen Ph. Lic., and Docent
Antti Virjo, who helped me with numerous technical problems.
I address warmest thanks to Heini Huhtala, M.Sci., for teaching and helping me to solve
the statistical problems of this study.
I thank Virginia Mattila, M.A., for checking the English language. I also thank Mervi
Ahola, Sisko Kammonen and Raila Melin from the Medical Library of Tampere
University Hospital for help in collecting the literature for this thesis.
Finally, I wish to thank my husband Jukka, for taking care of everyday routines during the
intensive time of writing. In addition, he always had time to help me with my personal
computer. I also thank our children, Saara-Maria and Riku-Matias for their understanding
sympathy towards my work.
This study was financially supported by grants from the Medical Research Fund of
Tampere University Hospital and Biomed-2 BMH4-CT97-2040 (SIESTA) funded by the
EU Commission.
125
“For me, sleep has become a never-ending story.“
Pirkkala, September 2000
Sari-Leena Himanen
126
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APPENDIX
Table 23. Peak frequencies (Hz) of VASS stages and comparison between EEG derivations
Lead
Peak frequency
Median
Min
Max
Paired
comparison
Sign
Hz
WA
C4
Fp2
O2
10.2
2.3
10.2
7.0
2.3
7.0
10.9
10.2
10.9
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
***
***
SA
C4
Fp2
O2
9.4
9.4
10.2
3.1
2.3
7.8
10.9
10.9
10.9
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
***
***
WL
C4
Fp2
O2
2.3
2.3
3.1
2.3
2.3
2.3
10.2
4.7
11.7
C4 vs Fp2
C4 vs O2
O2 vs Fp2
**
*
***
WAF
C4
Fp2
O2
9.4
6.6
9.4
3.1
2.3
2.3
11.7
10.9
11.7
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
ns
**
SAF
C4
Fp2
O2
7.8
7.0
9.4
2.3
2.3
2.3
10.9
10.9
10.9
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
*
**
DL
C4
Fp2
O2
3.1
2.3
3.9
2.3
2.3
2.3
10.2
10.2
10.9
C4 vs Fp2
C4 vs O2
O2 vs Fp2
*
ns
**
S1VASS
C4
Fp2
O2
3.1
2.3
3.1
2.3
2.3
2.3
7.8
8.6
9.4
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
ns
ns
S2VASS
C4
Fp2
O2
2.3
2.3
2.3
2.3
2.3
2.3
3.9
7.8
5.5
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
*
ns
Friedman test for multiple comparisons between leads not shown.
Post hoc analysis between leads by Wilcoxon test for paired differences with Bonferroni correction.
ns = non significant, * = p<0.05, ** = p<0.01, *** = p<0.001.
140
Table 24. Topographical differences between the powers of the peak frequencies in VASS stages
Lead
Power of the peak
Median
Min
µV2
Max
Paired
comparison
Sign
WA
C4
Fp2
O2
2.21
2.08
3.46
1.34
1.16
2.09
3.05
10.62
6.12
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
***
**
SA
C4
Fp2
O2
2.01
2.02
3.47
1.32
0.92
1.39
3.28
2.72
5.49
C4 vs Fp2
C4 vs O2
O2 vs Fp2
**
***
***
WL
C4
Fp2
O2
1.16
1.98
1.07
0.80
1.12
0.72
2.42
10.38
1.51
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
ns
***
WAF
C4
Fp2
O2
1.87
1.81
1.62
1.01
0.99
0.98
2.94
6.20
2.71
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
*
*
SAF
C4
Fp2
O2
1.87
1.81
1.63
1.14
1.11
1.09
3.09
3.09
2.55
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
***
**
DL
C4
Fp2
O2
1.17
1.30
1.11
0.80
0.82
0.78
2.34
2.27
2.00
C4 vs Fp2
C4 vs O2
O2 vs Fp2
*
ns
***
S1VASS
C4
Fp2
O2
1.53
1.62
1.38
0.91
1.09
0.92
3.13
2.86
2.17
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
***
***
S2VASS
C4
Fp2
O2
2.32
2.04
1.90
1.24
1.11
1.03
3.40
3.75
3.12
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
**
*
Friedman test for multiple comparisons between leads not shown.
Post hoc analysis between leads by Wilcoxon test for paired differences with Bonferroni correction.
ns = non significant, * = p<0.05, ** = p<0.01, *** = p<0.001.
141
Table 25. Topographical comparison of the band powers of the five frequency bands within
VASS stages
Alpha band power
Lead
Median
Min
Max
µV2
Paired
comparison
Sign
WA
C4
Fp2
O2
8.99
8.16
11.71
5.89
4.87
9.08
13.43
13.83
19.40
C4 vs Fp2
C4 vs O2
O2 vs Fp2
**
***
***
SA
C4
Fp2
O2
8.09
7.71
11.76
5.37
3.99
7.31
13.84
11.61
17.58
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
***
***
WL
C4
Fp2
O2
4.83
4.81
4.97
3.53
3.53
3.56
8.05
9.73
7.50
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
ns
ns
WAF
C4
Fp2
O2
7.20
6.50
6.72
4.40
3.48
3.72
9.19
8.77
8.73
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
ns
ns
SAF
C4
Fp2
O2
7.21
6.53
6.81
4.47
4.39
4.52
10.59
8.66
8.86
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
*
ns
DL
C4
Fp2
O2
4.97
4.71
4.86
2.92
3.06
3.43
8.66
8.99
7.32
C4 vs Fp2
C4 vs O2
O2 vs Fp2
**
ns
ns
S1VASS
C4
Fp2
O2
5.14
4.60
4.89
3.76
3.28
3.72
8.54
8.57
7.71
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
ns
ns
S2VASS
C4
Fp2
O2
5.39
4.71
4.98
4.34
3.76
3.76
8.75
7.77
8.91
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
ns
ns
Aa2
C4
Fp2
O2
11.13
9.52
11.27
6.16
6.24
6.16
18.12
13.05
13.91
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
ns
*
EMGW
C4
Fp2
O2
6.81
7.42
7.89
3.49
3.60
3.36
16.22
16.53
52.43
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
ns
ns
C4
Fp2
O2
9.11
13.81
11.46
5.68
5.08
5.72
143.07
56.77
111.81
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
ns
ns
MTW
142
Table 25 continued
Theta band power
Lead
Median
Min
Max
µV2
Paired
comparison
Sign
WA
C4
Fp2
O2
4.76
6.00
4.46
2.65
2.99
2.80
10.17
19.92
9.02
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
***
***
SA
C4
Fp2
O2
5.22
5.13
4.81
2.51
2.35
2.78
9.85
9.91
8.96
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
***
**
WL
C4
Fp2
O2
4.15
5.08
3.62
2.59
3.61
2.46
7.55
22.49
6.77
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
***
***
WAF
C4
Fp2
O2
4.15
4.63
4.04
2.28
2.07
2.16
9.75
11.33
8.43
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
***
**
SAF
C4
Fp2
O2
5.40
5.36
4.59
2.45
2.72
2.18
9.45
8.73
8.21
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
***
***
DL
C4
Fp2
O2
3.82
4.17
4.01
2.34
2.81
1.83
9.00
9.05
7.74
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
***
**
S1VASS
C4
Fp2
O2
5.34
5.13
5.29
3.06
3.24
2.79
9.24
9.12
8.59
C4 vs Fp2
C4 vs O2
O2 vs Fp2
**
*
ns
S2VASS
C4
Fp2
O2
6.26
5.65
6.87
4.07
3.77
3.33
9.24
10.05
10.41
C4 vs Fp2
C4 vs O2
O2 vs Fp2
**
ns
*
Aa2
C4
Fp2
O2
6.14
5.96
5.54
3.62
3.39
3.20
11.66
11.54
8.91
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
ns
ns
EMGW
C4
Fp2
O2
5.44
8.34
5.56
2.85
3.93
2.02
20.23
33.24
103.09
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
ns
**
MTW
C4
Fp2
O2
11.51
20.82
16.22
4.31
5.88
4.18
211.39
94.99
154.00
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
ns
ns
143
Table 25 continued
Delta band power
Lead
Median
Min
Max
µV2
Paired
comparison
Sign
WA
C4
Fp2
O2
6.76
12.58
6.12
4.14
6.57
3.75
14.20
55.06
13.01
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
*
***
SA
C4
Fp2
O2
6.93
10.59
5.73
4.37
6.09
3.88
8.77
15.09
14.21
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
**
***
WL
C4
Fp2
O2
6.88
13.34
5.73
4.12
7.03
3.53
13.24
49.10
11.39
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
**
***
WAF
C4
Fp2
O2
6.02
10.02
5.37
3.03
4.23
2.35
9.45
32.74
26.01
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
*
***
SAF
C4
Fp2
O2
6.79
9.18
5.69
3.42
5.03
3.03
11.23
47.08
11.97
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
***
***
DL
C4
Fp2
O2
6.34
9.77
5.66
4.49
6.53
3.76
11.31
14.01
9.63
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
**
***
S1VASS
C4
Fp2
O2
7.68
10.88
6.97
4.98
6.30
4.56
12.12
16.52
12.77
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
*
***
S2VASS
C4
Fp2
O2
10.61
12.12
8.86
6.18
6.99
4.76
18.44
23.05
17.07
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
*
***
Aa2
C4
Fp2
O2
7.40
9.76
7.35
4.31
5.61
4.41
21.52
22.44
31.09
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
ns
**
EMGW
C4
Fp2
O2
15.64
28.21
15.33
6.94
11.10
5.52
68.67
87.79
521.52
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
ns
ns
MTW
C4
Fp2
O2
37.27
76.93
58.71
10.03
30.38
7.06
275.98
554.02
552.64
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
ns
ns
144
Table 25 continued
Sigma band power
Lead
Median
Min
Max
Paired
comparison
Sign
µV2
WA
C4
Fp2
O2
3.15
2.88
3.07
2.00
1.85
2.41
5.11
5.13
5.06
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
ns
***
SA
C4
Fp2
O2
3.06
2.78
2.97
1.64
1.40
1.75
4.78
4.87
5.04
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
ns
***
WL
C4
Fp2
O2
2.50
2.50
2.35
1.54
1.64
1.62
4.04
4.25
3.83
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
***
**
WAF
C4
Fp2
O2
2.98
2.42
2.66
1.21
1.00
1.40
4.19
4.44
4.09
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
**
ns
SAF
C4
Fp2
O2
2.67
2.40
2.50
1.70
1.54
1.56
4.64
4.69
4.20
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
***
ns
DL
C4
Fp2
O2
2.49
2.24
2.14
1.58
1.46
1.25
4.33
4.54
3.61
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
***
*
S1VASS
C4
Fp2
O2
2.52
2.38
2.29
1.60
1.55
1.41
4.54
4.70
3.96
C4 vs Fp2
C4 vs O2
O2 vs Fp2
*
***
ns
S2VASS
C4
Fp2
O2
3.11
2.67
2.68
2.33
1.92
1.96
3.97
4.09
4.39
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
*
ns
Aa2
C4
Fp2
O2
4.58
3.75
4.36
3.31
2.22
2.96
7.77
6.32
6.33
C4 vs Fp2
C4 vs O2
O2 vs Fp2
**
ns
ns
EMGW
C4
Fp2
O2
3.46
3.77
3.32
2.02
2.27
2.04
7.78
8.46
24.64
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
ns
ns
MTW
C4
Fp2
O2
5.01
7.18
7.02
2.47
2.21
2.03
62.30
20.55
58.40
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
ns
ns
145
Table 25 continued
Beta band power
Lead
Median
Min
Max
µV2
Paired
comparison
Sign
WA
C4
Fp2
O2
7.55
6.55
6.74
4.42
4.03
4.81
10.45
10.30
10.76
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
ns
**
SA
C4
Fp2
O2
6.96
6.22
6.27
4.12
3.41
3.85
9.62
9.49
9.48
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
ns
*
WL
C4
Fp2
O2
5.62
5.64
5.01
3.59
3.87
3.39
8.01
8.43
6.90
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
***
***
WAF
C4
Fp2
O2
6.16
5.73
5.55
3.05
2.95
2.94
8.62
8.62
7.41
C4 vs Fp2
C4 vs O2
O2 vs Fp2
***
***
ns
SAF
C4
Fp2
O2
5.95
5.49
5.42
3.32
3.41
3.12
8.37
8.70
7.68
C4 vs Fp2
C4 vs O2
O2 vs Fp2
*
***
ns
DL
C4
Fp2
O2
5.06
5.06
4.74
2.99
3.08
2.80
8.71
8.96
8.29
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
***
***
S1VASS
C4
Fp2
O2
5.44
5.35
4.85
3.12
3.20
2.74
8.75
9.01
7.40
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
***
***
S2VASS
C4
Fp2
O2
5.40
4.97
5.00
4.30
3.51
3.53
7.74
6.97
6.77
C4 vs Fp2
C4 vs O2
O2 vs Fp2
*
***
ns
Aa2
C4
Fp2
O2
8.21
7.27
7.08
5.78
4.53
5.10
12.19
10.21
10.93
C4 vs Fp2
C4 vs O2
O2 vs Fp2
**
**
ns
EMGW
C4
Fp2
O2
10.08
8.91
8.18
5.97
5.93
5.15
22.12
21.53
50.05
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
*
ns
MTW
C4
Fp2
O2
13.03
17.76
20.90
4.30
4.58
5.73
143.60
36.98
112.63
C4 vs Fp2
C4 vs O2
O2 vs Fp2
ns
ns
ns
Friedman test for multiple comparisons between leads not shown.
Post hoc analysis between leads by Wilcoxon test for paired differences with Bonferroni correction.
ns = non significant, * = p<0.05, ** = p<0.01, *** = p<0.001.
146
Table 26. Power band comparisons between VASS stages in Fp2, C4 and O2
Fp2
C4
O2
delta theta alpha sigma beta delta theta alpha sigma beta delta theta alpha sigma beta
WA-SA
**
**
ns
**
***
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
WA-WL
ns
ns
***
**
**
ns
***
***
***
***
ns
***
***
***
***
WA-WAF
***
***
***
***
***
**
**
***
***
***
**
*
***
***
***
WA-SAF
**
ns
***
***
***
ns
ns
***
**
***
ns
ns
***
***
***
WA-DL
**
***
***
***
***
ns
**
***
***
***
ns
**
***
***
***
WA-S1VASS
ns
ns
***
***
***
*
ns
***
***
***
*
ns
***
***
***
WA-S2VASS
ns
ns
**
ns
*
*
ns
**
ns
*
**
*
**
ns
**
WA-Aa2
ns
ns
ns
*
ns
ns
ns
ns
**
ns
ns
ns
ns
ns
ns
WA-EMGW
***
**
ns
***
***
***
ns
***
ns
***
***
ns
*
ns
*
WA-MTW
*
*
ns
*
*
*
*
ns
ns
ns
*
*
ns
*
*
SA-WL
***
ns
***
ns
ns
ns
***
***
***
***
ns
***
***
***
***
SA-WAF
ns
ns
***
*
*
ns
**
***
ns
*
ns
*
***
***
***
SA-SAF
ns
ns
**
ns
**
ns
ns
***
ns
**
ns
ns
***
***
***
SA-DL
ns
**
***
***
***
ns
***
***
***
***
ns
***
***
***
***
SA-S1VASS
ns
ns
***
**
**
**
ns
***
***
***
**
ns
***
***
***
SA-S2VASS
ns
ns
**
ns
ns
**
ns
**
ns
ns
**
*
**
ns
ns
SA-Aa2
ns
ns
ns
**
ns
ns
ns
ns
**
*
*
ns
ns
*
ns
SA-EMGW
***
***
ns
***
***
***
ns
**
*
***
***
ns
ns
ns
***
SA-MTW
*
*
ns
*
*
*
*
ns
ns
ns
*
*
ns
*
*
WL-WAF
***
ns
ns
ns
ns
ns
ns
***
**
*
ns
**
***
***
**
WL-SAF
**
ns
*
ns
ns
ns
**
***
ns
ns
ns
***
***
ns
ns
WL-DL
***
**
ns
ns
ns
ns
ns
ns
ns
ns
ns
*
ns
ns
ns
WL-S1VASS
*
ns
ns
ns
ns
ns
***
ns
ns
ns
ns
***
ns
ns
ns
WL-S2VASS
ns
ns
ns
ns
ns
**
**
ns
**
ns
**
**
ns
ns
ns
WL-Aa2
ns
ns
*
**
ns
ns
**
**
**
**
ns
*
**
**
**
WL-EMGW
***
**
***
***
***
***
***
***
***
***
***
***
***
***
***
WL-MTW
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
WAF-SAF
ns
ns
ns
ns
ns
ns
**
ns
ns
ns
ns
ns
ns
ns
ns
WAF-DL
ns
ns
**
ns
ns
ns
ns
**
*
*
ns
ns
***
**
*
WAF-S1VASS
ns
ns
***
ns
ns
***
**
***
ns
*
**
*
***
*
ns
WAF-S2VASS
ns
ns
ns
ns
ns
**
**
ns
ns
ns
ns
*
ns
ns
ns
WAF-Aa2
ns
ns
*
*
*
ns
*
*
*
*
ns
ns
*
*
*
WAF-EMGW
***
***
**
***
***
***
*
ns
***
***
***
**
*
***
***
WAF-MTW
*
*
ns
*
*
*
*
ns
ns
*
*
*
*
*
*
SAF-DL
ns
**
***
ns
ns
ns
**
***
*
ns
ns
*
***
*
*
SAF-S1VASS
ns
ns
***
ns
ns
*
ns
***
ns
ns
***
ns
***
ns
ns
SAF-S2VASS
ns
ns
*
ns
ns
*
ns
ns
ns
ns
**
*
ns
ns
ns
SAF-Aa2
ns
ns
*
*
*
ns
ns
*
*
*
ns
ns
*
*
*
SAF-EMGW
***
**
*
***
***
***
ns
ns
**
***
***
ns
ns
***
***
SAF-MTW
*
*
ns
*
*
*
ns
ns
ns
*
*
ns
ns
*
*
DL-S1VASS
ns
***
ns
ns
ns
**
***
ns
ns
ns
***
***
ns
ns
ns
DL-S2VASS
**
**
ns
ns
ns
**
**
ns
*
ns
**
**
ns
ns
ns
DL-Aa2
ns
*
**
**
*
ns
*
**
**
**
*
*
**
**
*
DL-EMGW
***
***
***
***
***
***
***
***
***
***
***
*
***
***
***
DL-MTW
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
S1VASS-S2VASS ns
*
*
ns
ns
**
**
**
**
ns
**
**
*
*
ns
S1VASS-Aa2
ns
ns
**
**
**
ns
ns
**
**
**
ns
ns
**
**
**
S1VASS-EMGW
***
***
***
***
***
***
ns
***
***
***
***
ns
***
***
***
S1VASS-MTW
*
*
*
*
*
*
*
*
*
*
*
ns
*
*
*
S2VASS-Aa2
ns
ns
*
*
***
ns
ns
*
*
*
ns
ns
*
*
*
S2VASS-EMGW
*
ns
**
*
ns
*
ns
*
ns
**
*
ns
*
ns
**
S2VASS-MTW
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
Aa2-EMGW
**
ns
ns
ns
*
**
ns
ns
ns
ns
*
ns
ns
ns
ns
Aa2-MTW
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
EMGW-MTW
*
*
ns
ns
ns
*
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns = non significant, * = p<0.05, ** = p<0.01, *** = p<0.001.
147