Sleep, 20(9):743-752
© 1997 American Sleep Disorders Association and Sleep Research Society
Sleep and Sleep States
.
Precise Measurement of Individual Rapid Eye Movements In
REM Sleep of Humans
Kazumi Takahashi and Yoshikata Atsumi
Department of Neuropsychiatry, Tokyo Medical and Dental University, Tokyo, Japan
Summary: An automated analyzer for individual eye movements (EMs) has been developed that enables precise
analyses of their incidence. Three new parameters for each EM are obtained: EM magnitude, the angle and speed
of eyeball rotation, and the energy of each EM. All rapid eye movement (REM) sleep EMs from 40 nights of
polysomnography for 20 healthy young men were analyzed. The mean frequency of eye movement (EM frequency)
was 15.9 per minute. Compared to conventionally analyzed rapid eye movement (REM) density, EM frequency
was more sensitive to differences among sleep cycles, nights, and individuals. The mean EM rotation was 6.27 :!:
0.021 degrees, the mean speed of rotation was 58.73 :!: 0.18 degrees/second, and mean energy was 525.85 :!: 3.82
degrees'/second. The distribution of changes in these new parameters differed from conventional measures across
REM episodes. The conventional measures, REM episode duration, and REM density increased progressively in
successive REM episodes in an ascent-to-right pattern. However, the new parameters peaked in the second, followed
by relatively low values, producing an inverted V pattern. This discrepancy could indicate physiological mechanisms
of EM that are not revealed in conventional measures of REM sleep intensity. Key Words: Rapid eye movementsREM sleep-REM density-REM burst-REM sleep intensity.
Aserinsky and Kleitman (1,2) first described the rapid eye movements that occur during sleep periods
characterized by low-voltage fast electroencephalography (EEG). The proliferation of subsequent investigations of rapid eye movement (REM) and non-REM
(NREM) sleep has revealed much about the physiology of sleep and its disorders and about the pathophysiology of mental illness. The existing parameters
of REM sleep can be grouped into two categories.
Tonic parameters include the timing and duration of
REM sleep, such as the percentage of REM in total
sleep, REM latency, and the duration of each REM
sleep episode. Phasic REM sleep parameters include
the number (REM activity) and incidence (REM density) of rapid eye movements (EMs; hereafter, EM represents a single rapid eye movement in a REM period).
In this paper we focus on phasic REM sleep. The main
purpose of the present study was to extract additional
physiological information from more precise measurements of REM sleep.
To extract more information from the electrooculogram (EOG), we developed a new automated analysis
system for EMs that is also fit for clinical use. This
system enabled us to investigate three aspects of EMs
in REM sleep. First, to improve the accuracy of two
conventional parameters, REM activity and REM density, the exact number and timing of individual EMs
was determined. Second, on the basis of the measurement of the size of individual EMs, three new parameters of REM sleep were measured, which represent
the magnitude of each EM. Finally, the simultaneous
measurement of exact EM incidence and magnitude
enabled us to study the characteristics of EM bursts in
REM sleep. For comparison in future studies, we present standard values of these new parameters for young
healthy males. We also discuss the physiological basis
of both the new and older conventional parameters.
METHODS
Subjects and recordings
Accepted for publication July 1997.
Address correspondence and reprint requests to Kazumi Takahashi, M.D., Tokyo Metropolitan Matsuzawa Hospital, 2-1-1 Kamikitazawa Setagaya-ku, Tokyo 156, Japan.
Twenty healthy normal male volunteers (24-28
years old) were polygraphically recorded for three
consecutive nights. Lights were turned off between
743
K. TAKAHASHI AND Y ATSUMI
744
a.
Raw
Data
.- ...... .
••••
•
First step;
The raw data are digitized at
80Hz and filtered with a 7-point
weighted moving average .
•••
••••••••
~
b.
...... /
A
t
...... .
Second step;
A saccadic eye movement is defined by
two obtuse angles at its onset and
termination (point A and B). The
duration of the EM is time(B) time (A) •
To identify point A and B, the data
are differentiated twice.
The first differential increases near
apex A and decreases near apex B.
The second differential identifies the
peak values of the apices.
B
c.
duration
Third step;
If segment AB exceeds three
thresholds for amplitude, duration
and slope an EM is registered.
The amplitude, duration and the time
of onset of EM are stored for
analysis.
FIG. 1. Detection of an eye movement (EM) mimics the human recognition process in three steps. Detection occurs in the second step,
when the electrocUlogram signal is differentiated twice. First step: Thc raw data are digitized at 80 Hz and filtered with a seven-point
weighted moving average. Second step: A saccade eye movement is defined by two obtuse angles at its onset and termination (points A
and B). The duration of the EM is time (B) - time (A). To identify points A and B, the data are differentiated twice. The first differential
increases near apex A and decreases near apex B. The second differential identifies the peak values of the apices. Third step: If segment
AB exceeds three thresholds for amplitude, duration, and slope, and EM is registered. The amplitude, duration, and time of onset of EM
are stored for analysis.
2300 and 0100 hours in accordance with each subject's
daily routine. Subjects were awakened 7.5 hours after
lights were turned off. EEG (FpI, Fp2, C3, C4, 01,
and 02 referenced to linked Al + A2), electromyographic (EMG, mentalis), and EOG activity were monitored. Excluding the first night's records, 40 nights'
data were obtained for analysis.
Horizontal and vertical EMs were recorded using
four electrodes: two at the outer canthi for horizontal
movements, and two placed periorbital1y for vertical
movements. Only the horizontal bipolar EOG was
used in the present automatic analysis. EOG was amplified by an amplifier coupled with alternating current
(AC), with a long time constant (TC) of 3.2 seconds.
Calibration of EM amplitude was performed each
night, just before the subjects were told to go to sleep,
as follows: Three small green LED targets were arranged horizontally on the ceiling above the subject.
Each subject lay on his back in bed and was instructed
to move his gaze to the single lighted target as each
target was lit. The distance between the left, middle,
Sleep, Vol. 20, No.9, 1997
and right LEDs was designed to obtain 10 degrees of
eye movement.
EM detection and measurement
We devised an algorithm for the detection of EOG
deflections that mimics human pattern recognition.
Long TC recordings of EOG produce deflections that
have an abrupt beginning and sudden termination. Humans are able to extract these characteristics, ignoring
electrical noise and maintaining a subjective threshold
for identifying EMs. We simulated this process by decomposing it into three steps (Fig. la-c). Horizontal
EOG records were digitized at a sampling rate of 80
Hz. The first step filtered the signal using a seven-point
weighted moving average to eliminate jiggling electrical noise (Fig. Ia). The second step contained the main
identification process (Fig. I b). Candidate EMs are defined by an abrupt beginning (A) and sudden termination (B) of activity. To detect apices A and B, differential calculations were used. Figure I b shows the
PRECISE MEASURES OF RAPID EYE MOVEMENTS
corresponding effects of the first- and second-order
differentiations. Threshold criteria for identifying apices A and B were determined empirically. In the third
step (Fig. Ic), the actual detection of EMs was performed by applying criteria to each EM candidate
identified in step two. EMs were required to fulfill
three criteria of amplitude (>30 microvolts = 1.05 mm
on paper recordings), duration «0.5 second), and
slope (>248.3 microvolt/second = 30 degrees on paper recordings). These criteria were established on the
basis of our visual inspection of the long TC EOG.
, The amplitude, time of occurrence, and duration of all
suprathreshold EMs were measured on the raw waves
(not on the differentiated data) and then stored on a
computer disk. Finally, after the on-line analyses were
complete, each EM amplitude was converted into degrees of eyeball rotation on the basis of the prerecording IO-degree calibration of eyeball shift. The system
was implemented on a PC-980I NEC personal computer with analog-to-digital converter board.
Data analysis
Visual sleep stage scoring was performed for each
20-second epoch according to the criteria of Rechtschaffen and Kales (3). Only the first four REM episodes were analyzed in the present study. To define
NREM-REM cycles, a IS-minute rule was applied
that defined two successive REM periods separated by
more than 15 minutes as distinct REM episodes (46). Epochs in which the EOG channels contained
EMG or artifacts were visually identified and eliminated before the statistical analyses. Four first REM
episodes from three subjects were judged to be missed.
In one of two recordings of a subject there were only
three REM episodes; the subject awoke in the fourth
NREM period. Thus, a total of 155 REM episodes
were analyzed over 40 nights.
745
of each EM; 5) EM rotation, degrees of eyeball rotation; 6) EM velocity, the angular velocity (degrees!
second) of eyeball rotation; 7) EM power, defined as
(EM rotation)2lEM duration; EM power is an estimate
of the total energy expended during an EM (7,8).
Large and quick EMs consume more energy than small
and slow EMs.
REM burst analyses
We tried to automatically detect bursts of REM activity in which the time between any two successive
eye movements was less than a critical value (9). We
defined a "burst" as a group of EMs in which the time
between any two successive EMs was less than a critical value (Tl) and which contained more than a critical number of EMs (Cl). We determined Tl and Cl
empirically by visually scoring 93 minutes of REM
sleep EOG tracings from three subjects. Working independently, two human scorers visually detected EM
bursts. Then, Tl and Cl were defined to produce the
best agreement between the resulting computer analyses and the human scoring. With TI = 3 seconds and
Cl = 7, the sensitivity of the computer analysis to
testers A and B was 85.2 and 87.8%, respectively.
Specificity, computed in the same manner, was 80.4
and 73.8%, respectively.
Statistics
Two methods were used to assess the significance of
differences between REM episodes. First, differences in
the durations of REM episodes were assessed by analysis
of variance (ANOVA) using Fisher's protected least significant difference (PLSD) for post-hoc tests. Second,
because EM frequency, EM rotation, EM velocity, and
EM power each showed large interindividual variations,
each night's values were standardized into a normal distribution by z transfonnation before being assessed by
ANOVA using Fisher's PLSD for post-hoc tests.
Definition of EM measurements
Three basic measures were obtained from computer
analysis: the time of onset, duration, and amplitude of
each EM. Four refined measures of conventional parameters, and three new measures of the magnitude of
EM, were derived from these basic measurements as
follows: 1) EM count, the total number of EMs in a
period of time (e.g. each epoch, each REM episode,
an entire night); 2) EM frequency, the number of EMs
per minute; 3) EM interval, the time between two successive EMs, i.e. the interval between the end of an
EM and the beginning of the next EM (the EM interval
between REM episodes was not computed; 4) EM duration, the time between the onset and the termination
RESULTS
System validation
To assess the reliability of the system, 93 minutes
of REM sleep from three subjects other than those
used to determine thresholds Tl and Cl were analyzed
by computer and two human scorers, and the results
were compared. The two scorers were psychiatrists experienced in sleep research and the visual inspection
of polysomnograms. They were instructed to independently mark each EM. (The criteria for the detection
of EM are the same as the described computer algorithm: EOG amplitude> 1.05 mm, EOG duration <0.5
Sleep, Vol. 20, No.9, 1997
K. TAKAHASHI AND Y. ATSUMI
746
EM-frequency (count/min)
35
30
25
20
15
10
o~------------------------------------------------
subject
FIG. 2. Interindividual and intraindividual variations in eye movement (EM) frequency, with each subject's second and third night values
connected. Variability of EM frequency was much less within individuals than among individuals.
second, and slope >30 degrees on paper recordings.)
The same EOG data were analyzed by the computer
system. Scorers A and B and the computer counted
2,002, 2,223 and 2,303 EMs, respectively. The concordance between the computer and scorers A and B
was 87.0 and 84.2%, and between A and B it was
84.8%. The concordance was calculated as the percentage of EMs that both computer and human recognized of the total EMs that either of them recognized. For EMs counted per 20-second epoch in the
279 epochs, the linear correlation coefficients between
the computer and scorer A (r = 0.942, p < 0.0001),
computer and scorer B (r = 0.955, P < 0.0001), and
scorers A and B (r = 0.983, p < 0.0001) were all
highly significant.
Incidence of EMs
REM density and EM frequency
After excluding artifact epochs, the total duration of
the first four REM episodes (REMPs 1-4) for the 40
night recordings was 3,368.3 minutes (84.2 minutes/
night), and the total EM count was 53,562. The EM
count for each night varied widely; mean 1,342.9,
range 258 to 3,727, and standard deviation (SO) 767.1.
The grand mean EM frequency was 15.9 counts/minute. The mean EM frequency for each night ranged
from 5.54 to 33.38 counts/minute (SO = 8.11). Figure
2 shows interindividual and intraindividual variations
in EM frequency, with each subject's second and third
night values connected. Variability of EM frequency
was much less within individuals (SO = 2.75) than
Basic REM sleep parameters
among individuals (SO = 48.73). For 14 of the 20
Visual sleep stage scoring confirmed the expected subjects, the SO differed between the second and the
amount and distribution of REM sleep. The mean total third nights by less than 1 second.
Table 1 compares the results from our automatic ansleep time for the first four NREM-REM cycles was
386.7 (±41.6) minutes, with a mean of 94.4 (±24.6) alyzer to conventional REM parameters. Because it
minutes of total REM sleep. The mean percentage of was time consuming and practically impossible to
REM sleep in the total sleep of four NREM-REM count the exact number of EMs by visual inspection,
the values of REM activity and REM density have
cycles was 24.4%.
The mean durations of the first four REM periods been substituted for the true number and incidence of
were 16.2 (± 13.3), 26.4 (± 15.5), 34.4 (± 18.7), and EMs (9); true values are thought to be best represented
33.6 (± 19.9) minutes, respectively. REM episodes be- by EM count and EM frequency. To calculate REM
came progressively longer during the night. Ourations density from the computer analyzed data, the total of
of the first REM episodes were significantly shorter 155 REM periods were divided into 20, 10, 5, and 1
than the second, third, and fourth REM episodes, and second epochs, and each was examined for the pressecond REM episodes were shorter than the third and ence of at least one EM. Comparing REM density to
fourth (ANOVA, P < 0.05).
EM frequency, the correlation coefficients were 0.466,
Sleep, Vol. 20, No.9, 1997
747
PRECISE MEASURES OF RAPID EYE MOVEMENTS
TABLE 1.
REM activity and REM density
Analysis epoch duration (seconds)
REM activity
REM density
Correlation of REM density vs. EM frequency
20
10
5
190.85
0.71
295.68
0.55
440.83
0.41
0.671
0.466
0.824
EM countlEM frequency
967.68
0.18
0.972
1,833 (EM count)
20.4 count/minute
(EM frequency)
1.000
REM. rapid eye movement; EM, eye movement.
0.617,0.824, and 0.972 for analysis epochs of 20, 10,
5, and 1 second, respectively.
Sensitivity of new measure EM frequency to REM
density
To assess the sensItIvIty of our new measure EM
frequency to conventional REM density, relative values were calculated for each measure by dividing each
value by the corresponding value from REM period
one. Figure 3 presents an example of the relative
changes in REM density and EM frequency across
REM periods from a representative 25-year-old male
(subject 14). All calculations of REM density increased significantly between the second and third
REM episodes (ANOVA, p < 0.05). REM density calculated for 20-second intervals did not increase significantly between the second and fourth REM episodes. However, when computed for epochs shorter
than 10 seconds, REM density showed significant increments between REM periods two and four (ANO-
VA, P < 0.05). Only EM frequency increased significantly from the second to third, second to fourth, and
third to fourth REM episodes (ANOVA, p < 0.05).
New measure EM frequency can detect fine changes
between REM episodes.
EM interval
The grand mean EM interval was 3.74 seconds
(range 0-409 seconds). Figure 4 shows the frequency
distribution for EM interval values between 0 and 12
seconds. The modal interval was 0.2-0.4 seconds;
80% of the EM intervals were less than 2.6 seconds
long, and 90% were less than 7.0 seconds long.
Magnitude of single eye movements
Table 2 presents the mean characteristics of computer-analyzed EMs for 40 nights. The mean EM duration was 0.115 second. The modal EM duration fell
into the 75-100 microsecond analysis bin (46.9% of
relative value
II
9
7
5
•
•
3
EM-frequency
REM density calculated for
I-sec analysis epoch
.. REM density calculated for
5-sec analysis epoch
o
~
2
4
REM density calculated for IO-sec analYSis epoch
REM density calculated for 20-sec analysis epoch
REM period
FIG. 3. An example of the relative changes in rapid eye movement (REM) density and eye movement (EM) frequency across REM
periods from a representative 25-year-old male. All values of REM density increase during the night but differ from EM frequency. New
measure EM frequency can detect fine changes between REM episodes.
Sleep, Vol. 20. No.9, 1997
l
K. TAKAHASHI AND Y. ATSUMI
748
%
rotation, EM velocity, and EM power in bursts were
significantly larger than were isolated EMs (ANOVA,
p < 0.001). That is, EMs in bursts were larger, rotated
more quickly, and had greater power than isolated
EMs.
14
12
10
8
Changes in REM measures across REM episodes
6
4
2
sec.
0
0
2
4
6
8
10
12
FIG. 4. Frequency distribution for eye movement (EM) interval
values between 0 and 12 seconds. The modal interval was 0.2-0.4
second; 80% of the EM intervals were less than 2.6 seconds. The
distribution of EM intervals lacks a bimodal distribution.
EMs were in this bin). The mean EM rotation was 6.27
degrees. The modal EM rotation was 2-3 degrees
(18.9% of EMs were in this bin), with 80% of the
values less than 9.0 degrees and 91 % less than 13.0
degrees. The mean EM velocity was 58.73 degrees/
second. The mode was between 30 and 40 degrees/
second (17.7% of EMs were in this bin, with 83% less
than 90 degrees/second and 91 % less than 120 degrees/second. Mean EM power was 525.85 degrees 2/
second. For EM power values, the mode was between
o and 100 degrees2/second (26.4% of EMs were in this
bin), with 82% less than 800 degrees 2/second and 90%
less than 1,400 degrees 2/second.
REM bursts
Table 3 compares the characteristics of isolated EMs
with those in bursts. Of the 53,562 EMs detected in
40 nights, 37,383 (69.8%) belonged to REM "bursts"
and 16,179 (30.2%) were isolated EMs. The mean EM
interval in a burst was 1.40 seconds; for isolated EMs
the mean interval was 9.16 seconds. The mean number
of EMs that formed a burst was 16.5 (ranging from 7,
defined by C1, to 206). There was a mean of 0.70
bursts per minute in REM sleep over 40 nights, ranging from 0.14 to 1.74 counts/minute per night. EM
TABLE 2.
duration (seconds)
rotation (degrees)
velocity (degrees/second)
power (degrees squared/second)
EM(s), eye movement(s); SD, standard deviation.
a Threshold.
Sleep, Vol. 20, No.9, 1997
DISCUSSION
Automated EM analyzer
Our computer system was designed for clinical use
and relied upon three essential features to accurately
count and measure each EM: the use of horizontal
EOG, amplification of the EOG with a long TC, and
pattern recognition methods. The importance of these
features was learned by investigating many previous
studies of automated EOG devices (10-19). Horizontal
EOG has two advantages over vertical or oblique EOG
recordings. In clinical use, horizontal EOG is rarely
affected by artifacts such as high-voltage EEG or eyelid movements. Also, more than one-half of all EMs
occur in the horizontal direction (14,15). Long TC amplification is essential for the accurate measurement of
single EM amplitudes, because short TCs produce
large distortions of the EOG waveform. Compared to
direct-current (DC) recording, the AC method cannot
detect relatively slow frequency. However, a TC longer
than 3 seconds is enough for rapid EMs in REM sleep
(13,14). Pattern recognition methods can easily and
effectively recognize the waveforms characteristic of
Measurements of single EMs (n
Mean
EM
EM
EM
EM
Figure 5 shows the relative changes in the duration
of REM episode, EM frequency, and EM rotation
across REM episodes for all 40 recordings. REM episode duration and EM frequency increased significantly from the first to fourth REM episodes in an
ascent-to-right pattern. By contrast, although EM rotation increased significantly from the first REM episode to its peak value in the second REM episode, it
then decreased in the third REM episode, making an
inverted V pattern. EM velocity and EM power had
the same pattern as EM rotation. Thus, the most vigorous rapid eye movements occurred in the middle of
the sleep period.
0.115
6.27
58.73
525.85
53,562)
SD
Minimum
Maximum
0.061
4.86
40.9
884.0
0.050
1.23"
8.85
9.08
0.49
61.51
491.98
19,014.97
~
::':
::':
::':
::':
=
749
PRECISE MEASURES OF RAPID EYE MOVEMENTS
TABLE 3.
Comparison of burst EMs with isolated EMs
EMs in burst
Count (% of total)
EM interval (seconds)
EM rotation (degrees)
EM velocity (degrees/second)
EM power (degrees squared/second)
37,383 (69.8%)
lAO:!:: 5.74
6.77 :!:: 5.23**
63.21 :!:: 44.13**
611.57 :!:: 986.30**
Isolated EMs
All
16,179 (30.2%)
9.16 :!:: 21.97
5.13 :!:: 3.62
48.38 :!:: 29.76
327.81 :!:: 532.30
53,562 (100%)
3.74 :!:: 13A7
6.27 :!:: 4.86
58.73 :!:: 40.90
525.85 :!:: 884.0
EM(s), eye movement(s) .
.. p < 0.001.
EMs; all measurements of individual EMs depend on
precise recognition of these waveforms. Thus, the values of individual EM measurements depend critically
on both recording and analysis methods. Future studies
should clearly describe TC settings, EOG direction,
and analysis thresholds to allow comparisons between
the results obtained using different methods.
Of the eight studies described in Table 4 (9,15,2025), EM frequency ranged from 5.4/minute to 25.2/
minute in the studies of young adults. Aserinsky (9)
and Ehlers and Kupfer (24) reported much smaller EM
frequency values than we found in the present study.
This could be due to their use of short TC recordings,
which could not detect relatively slow and small amplitude EMs. Schneider's method (15) may have
counted more EMs than ours because of its use of long
*
35 min
*
REM episode duration
25
15
20 count/min
*
EM-frequency
15
10
EM-rotation
o
• p<O.05
.08
2
3
4
REM episode
FIG. 5. Changes in the duration of rapid eye movement (REM)
episode, eye movement (EM) frequency, and EM rotation across
REM episodes for all 40 recordings. REM episode duration and EM
frequency increased in an ascent-to-right pattern. By contrast, EM
rotation had its peak value in the second REM episode, making an
inverted V pattern.
TC and vectrooculogram (VOG) recordings, which
counted both horizontal and vertical EOGs. Not all
prior studies reported the validity of automated systems. Correlation coefficients between human scorings
and the automatic devices were 0.91 (19), 0.88-0.90
(26), and 0.19-0.93 (23). In our system, the correlations between two scorers and the computer system
ranged from 0.942 to 0.955. We also calculated concordances between each human scorer and the computer system to be 84.2 and 87.0%, which are very
similar to that between the two human scorers
(84.8%).
Precise measurement of EM incidence
The density of eye movements during REM sleep,
a rough estimate of EM incidence, has long been used
to evaluate mental characteristics such as intelligence
and mental development (27-29), depressive illness
(22,25,30,3 I}, schizophrenia (32), and aging (24,33).
Computer analysis can improve the accuracy of EM
incidence measures. In the present study, EM frequency was more sensitive to changes in EM incidence
across REM episodes than was REM density measured
in (the typical) 20-second and longer epochs. It is
therefore possible that reassessment of past studies that
did not indicate significant differences in REM density
could reveal significant changes in EM characteristics.
For example, McPartland et al. (22) measured EM frequency and studied primary depression. They reported
differences in EM frequency between normals and depressives, as well as fine changes in EM frequency that
correlated with the quantity of medication and severity
of symptoms. It has also been reported that use of
cholinergic agonists and antagonists alters the amount
of REM sleep and number of EMs (34). EM frequency
has a clear potential for application in such studies.
Besides whole-night measurements of REM density,
many studies have reported that patterns of REM density change across REM episodes. In healthy adults,
REM density usually increases progressively across
successive REM episodes in an ascent-to-right pattern.
This pattern reportedly differs for different mental
states; in an inverted V pattern during childhood that
changes to the ascent-to-right pattern in adolescence
Sleep, Vol. 20, No.9, 1997
K. TAKAHASHI AND Y. ATSUMI
750
TABLE 4.
Study (year)
Aserinsky (1971)
Ornitz et al. (1973)
Benoit et al. (1974)
Schneider (I 978b )
McPartland et al. (1979)
Coble et al. (1987)
Ehlers and Kupfer (1989)
Reynolds et al. (1990)
Prior studies of the frequency of EMs
EM frequency
(count/minute)
Time constant
8.4
15.0
16.8-24.6
25.4
6.6-10.8
7.2
5.4
4.2
Analog filter
AC filtered
0.3
3.0
0.3
0.3
0.3
0.3
Detection threshold
3-4 degrees
4
25
25
25
25
degrees
fLV
fLV
fLV
fLV
EOG
H
H, V
H, V, 0
VOG
H
H
H
H
Subjects (age)
10
8
10
12
23
17
8
15
(student)
(child)
(19.5 years)
(36.7 years)
(40.6 years, depressed)
(14-15 years)
(21-30 years)
(72.8 years)
EM(s), eye movement(s); EOG, electrooculogram; AC, alternating current; H, horizontal; V, vertical; 0, oblique; VOG, vectrooculogram.
(23), in a V-shaped pattern in depressive illness
(25,35) that is partially normalized by the administration of antidepressant medication (22), in an inverted
V pattern reflecting the severity of Alzheimer's disease
(36), and in a flat pattern in narcoleptic patients (37).
More detailed classifications of these patterns could be
obtained based on EM frequency.
Measuring the magnitude of single EMs
Rotation, velocity, and power are new parameters
representing the magnitude of individual EMs. Although EOG amplitUde directly expresses the changes
in polarity of the corneoretinal potential associated
with shifts of eyeball position, that potential changes
over time, especially during sleep. Thus, the relationship between the potential and the angle of rotation is
imperfect. However, the deviation is small, and the
linearity between the potential and the angle is maintained for movements less than 30 degrees (38,39).
Aserinsky et al. (40) measured 12 students (with TC
= 2.2 seconds) and reported that the mode of EM rotation was 4 degrees, with 90% of EMs less than 11.5
degrees. In the present study 90% of EM rotations
were smaller than 13.5 degrees, supporting our simple
conversion of EOG amplitude to angle of rotation
based on each night's calibration.
EM velocity expresses the angular velocity of eyeball rotation. Fukuda et al. (41,42) studied five healthy
young males (horizontal EOG with TC = 0.3 second)
and reported that 80% of EM velocity was distributed
in the range greater than 60 degrees/second. These values are larger than ours, probably because they could
not detect small EMs with the short TC used in their
recordings. Ornitz et al. (20) measured EMs of 3-10year-old children and reported that most were between
130-270 degrees/second. That finding suggests there
may be age related differences in EM velocity.
The variation in EM rotation and EM velocity
showed much larger differences among individuals
than within individuals. Mean EM rotation ranged
from 4.57 to 8.60 degrees (mean = 6.30, SD = 1.22),
and EM velocity ranged from 37.56 to 81.29 degrees/
Sleep, Vol. 20, No.9, 1997
second (mean = 58.55, SD = 10.51). However, 14 of
20 subjects had very similar values in repeated nights;
differences between the second and third nights were
only as high as 0.81 degree of EM rotation and 7.1
degrees/second of EM velocity. These 14 subjects also
had very stable EM frequency, as seen in Fig. 2. Thus,
it appears the frequency and magnitude of EMs have
a tendency to maintain constant values within a given
subject. These results suggest it is necessary to assess
both intraindi vidual and interindi vidual variances
when analyzing precise measures of EM frequency
and magnitude.
Measurements of REM bursts
Aserinsky (9) reported the distribution of intervals
of successive EMs to be bimodal, with one population
of shorter intervals (less than 4-8 seconds) and a second of longer intervals. He suggested the first population represented the intervals within REM "bursts"
and the second those of isolated EMs. In the present
study, we did not find a bimodal distribution (Fig. 4).
Our result does not differentiate the group of EMs conventionally called "bursts". On the other hand, on the
basis of visual inspection we defined another condition
that differentiated isolated EMs from those within
bursts. EMs within bursts not only had shorter intervals but larger rotations and greater velocity. As discussed below, this discrepancy could be studied in detail to relate the physiological properties underlying
the frequency of EMs and the intensity of REM sleep.
Intensities of REM sleep
Automated analyses of the amount of delta wave
activity have for many years been used to reflect the
"intensity of NREM sleep". The integrated amplitude
of delta waves measured by period-amplitude analysis
[advocated by Feinberg (4)] and the power density of
the delta band measured by fast Fourier transform
analysis [advocated by BorMy (43)] have mainly been
used. It is well established that however measured,
maximum delta wave activity occurs in the first
751
PRECISE MEASURES OF RAPID EYE MOVEMENTS
NREM sleep episodes, typically followed by a gradual
decline across subsequent NREM sleep periods in a
descent-to-right pattern.
On the other hand, the "intensity of REM sleep"
has usually been expressed by two parameters, the duration of REM sleep and the density of REM eye
movements (REM density). As shown in Fig. 3, these
conventional parameters usually increase progressively
across the REM periods in an ascent-to-right pattern
(21,23,33,36,37,44,45). This pattern implies that the
intensity of REM sleep increases across the night and
is highest in the early morning.
In the present study we measured new parameters
of REM sleep, the rotation and velocity of individual
eye movements. In relation to REM sleep intensity,
what do these new measures represent? Do they represent the intensity of REM sleep in ways comparable
to conventional REM density? If so, these new measures should parallel conventional measures with an
ascent-to-right increase across REM episodes. Alternatively, the magnitude of EMs might reflect mechanisms or processes different from conventional measures of REM sleep intensity. This seems to be the
case, given the discrepancies between our new measures and conventional parameters.
Our data show that the magnitude of EMs did not
change during the night in the same manner as conventional REM intensity. Compared to EM frequency
(REM density), which has an ascent-to-right pattern,
the magnitude of EM had its peak values in the middle
of the sleep period, producing an inverted-V pattern
(Fig. 5). This suggests the magnitude of EM reflects
physiological mechanisms that are not described by
the parameters conventionally used to describe REM
sleep intensity.
Examination of REM bursts may help clarify the
physiological meaning of the magnitude of EM in the
concept of REM sleep intensity. Within a REM episode, EM frequency and magnitude always change in
the same direction; during a REM burst both have high
values, whereas EMs outside of bursts have low values. During REM bursts EOG activity may reflect particular patterns of brain neuron activity. For example,
within REM bursts recorded from animals, pontogeniculo-occipital (PGO) frequency closely corresponds
to EM frequency and is highly correlated with the intensity of neuronal activity. Comparisons of the relationships between precise EM measurements and findings such as PGO wave activity might strengthen comparisons of animal studies and human polysornnography (46,47).
Combinations of these measures could be used as
parameters for investigations of mental activity during
REM sleep. It is known that REM intensity can reflect
different mental disorders such as schizophrenia (48)
and depressive illness (25,30,35,49). Automated measurement of the magnitude of single EMs in these
mental disorders could provide much additional information in such studies of REM sleep in the future.
New brain imaging technologies such as positron
emission tomography, functional magnetic resonance
imaging, and near-infrared spectroscopy will soon reveal images of neuronal activity during REM sleep
(50,51). Precise EM measures could be used as concurrent measures of REM intensity.
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