Sleep
10(6):505-507, Raven Press, Ltd., New York
© 1987 Association of Professional Sleep Societies
Editorial
At times an article raises controversies, being praised by one reviewer and strongly
opposed by another. This was the case with the two articles submitted by the Sleep
Research Laboratory of the University of Chicago. Several experts in the field disagreed about the methodology: could the technique used properly detect and quantify
fast EEG waves such as spindles coincident with, and riding on the top of, delta?
Should other manipulations have been done before performing period-amplitude analysis? Several revisions later, due to controversies still present, the editorial decision
was made to publish the two articles and let the readership judge for itself. We also
asked Dr. Ktonas, who had not been involved in the controversy, to review the different opinions expressed and to write an editorial outlining the points raised by the
reviewers.
Christian Guilleminault
Editor-in-Chief
Editorial Comment: Period-Amplitude EEG Analysis
The Editors of SLEEP asked me to write an Editorial Comment related to the following papers, "Period-Amplitude Analysis of Rat Electroencephalogram: Effects of
Sleep Deprivation and Exercise," referred to as Paper 1 and "Period-Amplitude Analysis of Rat Electroencephalogram: Stage and Diurnal Variations and Effects of Suprachiasmatic Nuclei Lesions," referred to as Paper 2. These are interesting papers,
especially since they provide data implying the possible existence of several parallels
between rat EEG and human EEG. However, some of this data should be viewed with
caution because they result from a methodology which may provide misleading information.
One of the main objectives of the authors, especially in Paper 1, was to quantify
separately the incidence and the amplitude patterns of individual EEG waves in
various wavelengths corresponding to EEG frequencies in the delta up to the beta
bands. For this task they correctly chose the technique of Period-Amplitude Analysis
(PAA). This method can quantify separately incidence and amplitude patterns of waves
within a specific EEG band, while other methods such as power spectrum analysis or
zero-cross analysis do not provide this kind of information.
Power spectrum analysis indicates the amount ofpower/energy (or the mean squared
value) within a frequency band, thus confounding patterns in wave amplitude and wave
incidence. In this way, it may not discriminate between two EEG epochs, one, say,
having a few delta waves of high amplitude, and another, having many more delta
waves but of a lower amplitUde. There are additional problems with power spectrum
analysis, especially concerning the correspondence of "frequency" as used in this
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EDITORIAL
technique with "frequency" as used in visual EEG analysis (1). On the other hand,
zero-cross anaiysis (i.e., counting the number of zero-voitage baseline crossings over a
unit of time) does not quantify amplitude and, furthermore, it may give erroneous information. For example, a count of 10 negative-to-positive zero-crossings in a one-sec
EEG epoch may indicate the presence of lO-cps alpha activity throughout the epoch or
the presence of several short bursts of beta activity, each lasting a fraction of a second.
For the above reasons PAA is the optimum signal analysis technique to be used for the
problem at hand.
However, as the authors point out in Paper 2, PAA may have deficiencies, in that it
may not detect the presence of fast EEG waves riding on top of slower activity. In this
case, PAA, as implemented by the authors, will detect only the slow EEG activity. On
the other hand, if appropriate bandpass filtering of the EEG data is done before using
PAA, then the PAA method can accurately detect both fast and slow activities. In this
vein, for the particular example of "alpha-delta" sleep which is mentioned by the authors, one could proceed as follows: First, the EEG signal is prefiltered with two bandpass filters in parallel, one passing activity between, say, 0.1 and 5 Hz, and the other
passing activity between 6 and 14 Hz. Then the application of PAA at the output of
these bandpass filters could detect separately individual waves within the delta and the
alpha EEG band. The reason one would not use tighter filters (i.e., of smaller bandwidth) is to avoid distortion of the filtered data, since the input to the filter may not be
a "pure" sinusoid, as well as to avoid "ringing" of the filter in the presence of highamplitude impulse-like input. This "ringing," which could be due to artifacts in the
data (e.g., electrode "pop"), could generate waves at the output of the filter which
would be mistaken for bona fide EEG waves by the PAA method. It should be emphasized that the objective of bandpass filtering before PAA is not to detect the EEG
activity in question (e.g., alpha band). Bandpass filtering is performed to enable the
PAA method itself to correctly detect the specific EEG activity. Accordingly, bandpass
filtering serves for signal conditioning in that it provides a baseline level to be used for
the measurement of the period and amplitude of successive EEG waves at the output of
the filter. It is the magnitude of these quantities, quantified by PAA, as well as the
pattern of their sequency, if appropriate, which make the detection of the EEG activity
in question possible. For example, for sigma spindle activity to be detected by an automatic system utilizing bandpass filter-based PAA, the bandwidth (3-db points) of the
bandpass filter is set between 8 and 25 Hz (2). Obviously, the output of such a filter
may include other activity in addition to sigma spindles. In the digital domain, the
bandpass filters can be easily made to have a linear phase response (finite impulse
response-FIR filters) for no phase distortion of the filtered data. Also, care should be
taken to make the roll-off of the filters steep enough for proper attenuation of unwanted
frequencies. However, since some residual contribution of the unwanted frequencies
may provide at the output of the filters a swaying baseline on which the waveform to be
analyzed by PAA will ride, instead of measuring the distance between alternative zerocrosses one can measure the distance between consecutive peaks or troughs of the
signal for individual wave period quantification. In such cases, a peak-to-peak amplitude measurement may be preferred. There are references in the literature on implementing PAA for sleep EEG analysis, elaborating on the above points (2-5). Based on
the above discussion, the statement of the authors in Paper 2 that" ... PAA cannot
differentiate frequency components because it is in the time domain ... " is not necessarily an appropriate one.
Sleep, Vol. 10, No.6, 1987
EDITORIAL
507
The authors argue against the above outlined procedure stating that" ... it requires
a priori selection of frequency ranges, rather than their post hoc determination .... "
This is a valid criticism, however, if one is interested in quantifying incidence and
amplitude patterns for individual EEG waves belonging to specific, well-defined EEG
frequency bands (e.g., delta, alpha, beta), then the bandpass filter-based method of
PAA may be used for the detection of such EEG activity (e.g., alpha bursts) and for the
quantification of period-amplitude patterns of waves within it. For their work, the authors could have defined several rat EEG frequency bands a priori and then could have
used a bandpass filter-based PAA approach for their study.
The PAA method as implemented by the authors is sensitive primarily to the slow
waveforms in cases where there may be fast EEG activity riding on top of slower
activity. Accordingly, their data on fast EEG waves (e.g., faster than 8 Hz) may not be
that accurate, in that their signal analysis may have missed activity in that range.
Therefore, their finding that as NREM wave incidence below 4 Hz increased incidence
above 5 Hz decreased could be an artifact of their methodology. In addition, due to
possible superimposed fast activity, the determination of the amplitude and period
length of slow EEG waves may have not been that accurate. Future work in the area,
utilizing alternative methods of signal analysis, could ellucidate these problems. Obtaining unequivocal data is important, especially if one is interested in proposing neurophysiological mechanisms responsible for sleep EEG generation.
Periklis Y. Ktonas
Department of Electrical Engineering
University of Houston
Houston, Texas
References
I. Ktonas PY, Gosalia AP. Spectral analysis vs. period-amplitude analysis of narrowband EEG activity: A
comparison based on the sleep delta frequency band. Sleep 1981;4:193-206.
2. Principe JC, Smith JR. SAMICOS-A sleep analyzing microcomputer system. IEEE Trans Biomed Engr
1986;33:935-941.
3. Smith JR. Computers in sleep research. CRC Crit Rev Bioeng 1978;3:93-148.
4. Hasan J. Differentiation of normal and disturbed sleep by automatic analysis. Acta Physiol Scand
[SuppI11983;526:1-103.
5. Principe JC, Smith JR. Design and implementation of linear phase FIR filters for biological signal processing. IEEE Trans Biomed Engr 1986;33:550-559.
Sleep, Vol. 10, No.6, 1987