Directory UMM :Data Elmu:jurnal:B:Biological Psichatry:Vol48.Issue11.2000:
High Internight Reliability of Computer-Measured
NREM Delta, Sigma, and Beta: Biological Implications
Xin Tan, Ian Glenn Campbell, Laura Palagini, and Irwin Feinberg
Background: Computer analysis of the sleep electroencephalogram (EEG) waveforms is widely employed, but
there have been no systematic studies of its reliability.
Methods: The most commonly used computer methods are
power spectral analysis with the fast-Fourier transform
(FFT) and period amplitude analysis (PAA) with zero
cross or zero first derivative half-wave measurement. We
applied all three computer methods to the digitized EEG of
16 normal subjects who underwent 5 consecutive nights of
baseline (placebo) recording. We evaluated the internight
reliability of three non–rapid eye movement (NREM)
frequency bands of special importance to sleep research:
delta (0.3–3 Hz), sigma (12–15 Hz), and beta (15–23 Hz).
Results: Both FFT and the two methods of PAA gave
excellent internight reliability for delta and sigma. Even a
single night of recording correlated highly (r . .9) with
the 5-night mean. Beta reliability was lower but still highly
significant for both the PAA and the FFT measures.
Conclusions: Computer-analyzed sleep EEG data are
highly reliable. Period amplitude methods demonstrate
that wave incidence and period as well as amplitude are
reliable, indicating that the reliability of composite measures (FFT power, PAA integrated amplitude) is not solely
based on individual differences in EEG amplitude. The
high internight stability of NREM delta indicates that it
possesses traitlike characteristics and is relatively independent of day-to-day variations in state. Biol Psychiatry 2000;48:1010 –1019 © 2000 Society of Biological
Psychiatry
Key Words:
homeostasis
Sleep
EEG,
computer,
reliability,
Introduction
T
he value of computer measurement of sleep electroencephalogram (EEG) waveforms is now generally
accepted. The two most widely employed methods are
period amplitude analysis (PAA) with zero cross and zero
derivative algorithms, and power spectral analysis (PSA)
with the fast-Fourier transform (FFT). In spite of increasing use of these methods, there are few published data on
their internight reliability or on the absolute magnitudes of
the differences that occur across baseline nights. Such data
could help formulate experimental designs, such as including estimates of experimental power and decisions on the
number of baseline nights to record. Knowledge of the
absolute magnitudes of internight variation under baseline
conditions could also be useful for evaluating experimental effects reported in the literature.
We are currently carrying out a large-scale investigation
of the reliability of computer-measured sleep EEG under
baseline conditions in young adults. Our analyses include
a wide frequency range (0 –100 Hz) in both non–rapid eye
movement (NREM) and REM sleep. Here we present
initial results for three NREM frequency bands of particular interest for the biology of sleep: delta (0.3–3 Hz),
sigma (12–15 Hz), and beta (15–23 Hz). The delta band is
of interest because of its close relation to maturation and
aging (Feinberg et al 1967, 1981, 1990; Williams et al
1974) over the human life span and its central role in
homeostatic models (Borbely 1982; Feinberg 1974).
Sigma and beta are also of considerable interest. Organized spindles in 12–15 Hz are a distinguishing hallmark
of the NREM EEG. Next to delta, organized spindles are
the waveforms that show greatest decline between young
adulthood and normal old age (Guazzelli et al 1986). It is
of further biological interest that sigma and beta power
exhibit systematic dynamic relations to delta, relations that
have spurred considerable research interest (Aeschbach et
al 1997; Borbely 1998) since their initial descriptions by
Uchida et al (1991).
Methods and Materials
Subjects
From the Department of Psychiatry, University of California, Davis (XT, IGC, IF),
Psychiatry Clinic, University of Pisa, Pisa, Italy (LP), and Veterans Administration Northern California Health Care System, Martinez (IF).
Address reprint requests to Irwin Feinberg, M.D., University of California,
VA/UCD Sleep Lab TB 148, Davis CA 95616.
Received October 18, 1999; revised February 22, 2000; accepted March 2, 2000.
© 2000 Society of Biological Psychiatry
The data for these analyses were obtained in a study that
compared the sleep EEG effects of three g-aminobutyric acid–
ergic hypnotics to placebo. The drugs were zolpidem (10 mg),
triazolam (0.25 mg), and temazepam (15 mg), given a half hour
before sleep in capsules identical to placebo. Preliminary reports
0006-3223/00/$20.00
PII S0006-3223(00)00873-8
Internight Reliability of Sleep EEG
of the drug effects on NREM delta, sigma, and beta frequencies
have been presented (Feinberg et al 1995a, 1995b). The study
was comprised of four treatment arms with sleep laboratory
recording on 5 consecutive nights. In each arm, subjects received
one of the active drugs or placebo for the first 3 nights and then
placebo for the final 2 nights. Therefore, in one treatment arm,
subjects received placebo for 5 consecutive nights. These placebo data were used for the reliability analyses. Subjects were
students at University of California, Davis who gave informed
consent and were paid for their participation. There were 10 male
and six female subjects between the ages of 19 and 26 years
(mean 5 20.1, SD 5 2.5). All were nonsmokers, within 25% of
the desirable weight for their height (according to the Metropolitan Life Insurance Table), and in excellent health according to
medical and psychiatric evaluations and a laboratory screen. No
subject used alcohol or other drugs of abuse during the study, and
urine drug screens were routinely performed. Time in bed was
meticulously controlled, with subjects in bed from 11:30 PM to
7:00 AM on each recording night and for the 3 nights at home that
preceded the laboratory recordings. Daytime naps were prohibited. The protocol required 2 nights of polygraphic screening to
rule out apnea and myoclonus and to establish that subjects had
normal sleep latencies (SLs), total sleep time (TST), and stages
3– 4 sleep. Criteria for acceptance were SL mean for 2 nights ,
20 min, TST mean for 2 nights of at least 400 min out of the
450-min recording period, and combined stages 3– 4 $ 15%
TST.
Recording and Calibration
The C3-A2 EEG was recorded continuously with a Grass
(Quincy, MA) Model 78 polygraph. A half-amplitude lowfrequency filter was set at 0.3 Hz, and a high frequency filter at
0.1 kHz. The preamplifier output was digitized at 200 Hz. The
digitized values were saved to optical disk and analyzed with
PASS PLUS (Delta Software, St. Louis). Of particular importance for quantitative EEG studies is careful calibration. PASS
PLUS analyzed a calibrated 3.5-Hz, 200-mV peak-to-peak sine
wave before each night’s recording and scaled the PAA and PSA
measurements on each channel to this standard.
Analyses of Sleep EEG
Visual scoring was performed on the ink-written record as
required by the research protocol. The scoring was done on
30-sec epochs without knowledge of drug condition (“blind”) by
two raters, with discrepancies resolved by a third rater. Rechtschaffen and Kales (1968) criteria for sleep stages were
applied. Movement and other artifacts were also scored. Correspondence between the visual scoring of the ink-written record
and the computer measures was accomplished with a computergenerated time code written on the polygraph record every 10 sec
by the digital-to-analog converter (with PASS PLUS on-screen
scoring, this correspondence is automatic). The computer data
reported below are based on all visually categorized, artifact-free
epochs of NREM scored as stages 2– 4 on each baseline
(placebo) night. There were, on average, 598 NREM (stages
2– 4) epochs per night.
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2000;48:1010 –1019
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Table 1. Period Amplitude Analysis Measures and Their
Definitions
Zero cross measures for each frequency band
Number of half-waves (BLX; measured as number of baseline
(zero) crossings)
Time in band (TIM; sum of all half-wave durations, measured in sec)
Integrated amplitude (IAM; sum of all half-wave integrated amplitudes,
in mV z sec)
Curve length (CUL; sum of all half-wave, peak-trough amplitudes, in
mV)
Average sample amplitude (ASA; IAM/TIM, in mV)
Mean frequency (FRQ; [BLX/2]/TIM, in Hz)
Zero first derivative measures for each frequency band
Derivative half-wave count (DZX; the number of zero derivative halfwaves)
Derivative time in band (DTM; sum of all derivative half-wave
durations, in sec)
Derivative curve length (DCL; sum of all peak-trough voltage
differences, in mV)
Derivative frequency (DFQ; [DZX/2]/DTM, in Hz)
Computer Analyses
PAA WITH PASS PLUS. Two methods of detection and
measurement are simultaneously applied by PASS PLUS period
analysis: half-wave detection by successive crossings of zero
voltage and by successive zero first derivative points. Zero cross
(also called baseline crossing) analysis is more effective for slow
frequencies. Zero derivative analysis is more suitable for fast
EEG waves, which are often superimposed on slower activity
and do not cross zero voltage. The algorithms for both PAA
methods have been published, along with initial data on their
reliability and the reproducibility of the absolute values obtained
in similar groups (Feinberg et al 1978, 1980). Linear interpolation has been incorporated in PASS PLUS PAA algorithms since
their inception. Such interpolation greatly improves resolution of
wave periods (frequencies) without the processing and storage
costs of high sample rates (“oversampling,” J.D. March, unpublished manuscript). Both the zero cross and the zero derivative
PAA yield separate estimates for wave number, period, and
amplitude. From these, several biologically meaningful ratios
(e.g. amplitude/half-wave, mean frequency) can be computed.
Since most laboratories that use PAA apply only zero cross
algorithms, we report here the internight reliability data for zero
cross measures for sigma and beta as well as for delta. (Normally
our laboratory uses zero cross measures for delta and zero
derivative measures for all higher frequencies, including sigma
and beta.) The specific PAA measures and their definitions are
shown in Table 1 (for details, see Feinberg et al 1978).
PSA WITH PASS PLUS. Fast-Fourier transform was performed on 30-sec epochs of 5.120-sec Welch tapered windows
with 2.620-sec overlap. This yielded 12 windows per 30-sec
epoch. The bands used in the analyses here were 0.3–3 Hz for
delta, 12–15 Hz for sigma, and 15–23 Hz for beta. (The actual
frequency bands differ slightly from these nominal values: delta
is 0.29 –3.03; sigma is 12.01–14.94, and beta is 14.94 –22.95).
Fast-Fourier transform analysis yielded the classical measure
of power in mV2 z sec for each frequency band.
1012
X. Tan et al
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Results
Table 2 lists the group means and SEs for each night for
each computer measure. These means were remarkably
stable, being virtually identical on each of the 5 nights for
both PAA and PSA measures; however, stability of group
means across nights can mask large individual variations
because subjects whose values are higher on one night
could be offset by subjects whose values vary in the
opposite direction. Table 3, which presents the average
within-subject difference across successive nights, shows
that this did not occur. Night–to-night individual differences for each measure were quite small. There was no
trend for mean internight differences per subject to increase or decrease across the 5 consecutive baseline
nights.
Figures 1 and 2 plot the values across 5 baseline nights
for PAA integrated amplitude and FFT power in the delta
and sigma bands for the subjects with the highest and
lowest 5-night means. These subjects were chosen because
they might be expected to show the greatest variation
because of the tendency toward regression to the mean. In
fact, the values for even these extreme subjects were quite
stable across the 5 nights.
As would be expected from the remarkable stability of
the group means and the small within-subject differences
across nights, the reliability of these computer-measured
EEG frequencies as estimated by Pearson correlation
coefficients was quite high. These data are shown in Table
4, which presents the correlation coefficients for each
measure across successive nights for delta, sigma, and beta
computed for the average 30-sec epoch of NREM sleep.
The five zero cross measures of delta showed consistently
high internight correlations. There was no tendency for
their correlation coefficients to increase or decrease across
successive nights. Delta power with PSA showed a median
internight correlation of .857, quite close to the .880 for
delta integrated amplitude, the similar PAA measure.
Table 4 shows the internight reliability for sigma.
Although our laboratory uses zero derivative PAA measures for sigma, we also present the zero cross results,
since most laboratories that use PAA apply only zero cross
measurement. With the exception of derivative mean
frequency, which had low and statistically insignificant
correlations, both the zero cross and the zero derivative
measures for sigma showed high internight correlations.
The correlations for sigma FFT power, although not
significantly greater than those for the corresponding PAA
measures, were notably high, with a median of .977 and a
narrow range of .965–.985.
Table 4 also shows that internight correlations for PAA
measures of NREM beta EEG were also substantial.
Somewhat surprisingly, the reliability for the beta zero
cross measures was about equal to that of the zero
derivative measures. The median correlation of .706 for
beta power with PSA was somewhat lower than its PAA
equivalent (.822), although this difference was not statistically significant.
Figures 3–5 present night 1–night 2 scattergrams for
integrated amplitude (PAA zero cross) and FFT power for
delta, and derivative curve length (PAA zero first derivative) and FFT power for sigma and beta bands. Nights 1
and 2 were chosen a priori because they might be
expected to have the poorest correlations because of
readaptation effects, and thereby more strongly challenge
the reliability of the computer measures. Figures 3–5
demonstrate strong linear relationships and also show that
the correlation coefficients do not depend upon outlying
points. We also plotted scattergrams for all other correlations, and none depended upon outlying points. However,
a markedly aberrant point dominated the night 1–night 2
correlation for derivative mean frequency, reducing its
correlation coefficient to near zero (Table 4). With this
point removed, the correlation increased from 2.093 to
.787.
In designing sleep experiments, one important decision
is the number of baseline nights to record. We assumed
that, for most experiments, the largest practical number
would be 5. We therefore tested the number of baseline
nights required to obtain high correlations with the 5-night
mean. These results are shown in Table 5. For delta and
sigma FFT power and their PAA equivalents, any single
night, including the first, provided a high correlation with
the 5-night mean that was not appreciably increased by
adding additional nights. For PAA measures of amplitude,
incidence, and period, 2 baseline nights appeared to
improve the correlation. Beta waveform measures appeared to require 2–3 nights for adequate correlations with
the 5-night mean with both PSA and PAA.
Discussion
There are few published studies to which we can compare
our findings. The most extensive previous reliability
analyses of PAA zero cross measures were reported when
we described the PAA algorithms (Feinberg et al 1978,
1980). In the first study, correlation coefficients for delta
zero cross measures across 2 baseline nights for the
average 20-sec epoch of NREM were high, ranging from
a low of .86 to a high of .91 (N 5 20). It was presumably
this high reliability that allowed us to detect significant
correlations of delta measures with age, even within the
narrow range of 18 –23 years. In the second study, the first
four cycles of an extended night and a recovery night were
compared for zero cross measures up to 23 Hz. Despite the
fact that correlations were computed across different
Internight Reliability of Sleep EEG
Table 2. Means (SEs) for Each of 5 Consecutive Baseline (Placebo) Nights for Period Amplitude Analysis (PAA) Zero Cross and Zero Derivative Measures
and Fast-Fourier Transform (FFT) Power
BLX
TIM
IAM
CUL
ASA
FRQ
POW
Delta (0.3–3 Hz)
N1
N2
N3
N4
N5
49.5 (1.0)
49.8 (1.1)
50.5 (0.9)
50.5 (1.3)
50.2 (1.1)
14.8 (0.40)
14.9 (0.40)
15.2 (0.35)
15.1 (0.46)
15.0 (0.39)
343 (18.1)
349 (17.5)
352 (19.2)
355 (15.9)
349 (15.5)
6.96 (0.27)
7.25 (0.28)
7.23 (0.34)
7.33 (0.26)
7.23 (0.28)
23.1 (0.85)
23.3 (0.83)
23.1 (0.95)
23.6 (0.74)
23.2 (0.75)
1.68 (0.02)
1.67 (0.02)
1.67 (0.02)
1.68 (0.02)
1.68 (0.02)
156 (13.9)
160 (13.4)
162 (15.7)
165 (12.3)
159 (11.8)
Sigma (12–15 Hz)
N1
N2
N3
N4
N5
27.9 (1.53)
27.6 (1.56)
26.9 (1.37)
27.3 (1.51)
27.3 (1.47)
1.04 (0.06)
1.03 (0.06)
1.00 (0.05)
1.01 (0.06)
1.02 (0.06)
6.63 (0.55)
6.69 (0.58)
6.39 (0.51)
6.55 (0.59)
6.47 (0.53)
0.65 (0.05)
0.67 (0.06)
0.64 (0.05)
0.66 (0.07)
0.64 (0.05)
6.27 (0.26)
6.37 (0.29)
6.27 (0.26)
6.30 (0.29)
6.25 (0.27)
13.43 (0.01)
13.44 (0.01)
13.43 (0.01)
13.45 (0.01)
13.44 (0.01)
3.11 (0.40)
3.13 (0.42)
3.04 (0.40)
3.11 (0.38)
3.12 (0.37)
Beta (15–23 Hz)
N1
N2
N3
N4
N5
47.1 (2.61)
46.5 (2.57)
45.5 (2.05)
45.7 (2.76)
46.1 (2.25)
1.27 (0.07)
1.26 (0.07)
1.23 (0.06)
1.24 (0.08)
1.25 (0.06)
6.13 (0.51)
6.17 (0.56)
5.87 (0.42)
6.03 (0.64)
5.91 (0.44)
0.82 (0.08)
0.83 (0.09)
0.79 (0.06)
0.81 (0.10)
0.79 (0.06)
4.74 (0.19)
4.79 (0.21)
4.70 (0.20)
4.73 (0.23)
4.66 (0.20)
18.47 (0.02)
18.47 (0.02)
18.46 (0.03)
18.46 (0.03)
18.46 (0.03)
1.40 (0.09)
1.43 (0.08)
1.37 (0.11)
1.43 (0.12)
1.40 (0.11)
DZX
DTM
DCL
DFQ
82.7 (4.25)
80.6 (4.47)
80.4 (3.77)
80.2 (4.07)
81.4 (4.23)
3.12 (0.16)
3.04 (0.17)
3.04 (0.14)
3.01 (0.15)
3.07 (0.16)
1.74 (0.80)
1.72 (0.94)
1.69 (0.91)
1.72 (0.89)
1.72 (0.96)
13.24 (0.03)
13.26 (0.02)
13.23 (0.04)
13.30 (0.02)
13.26 (0.03)
3.31 (0.23)
3.33 (0.22)
3.32 (0.16)
3.41 (0.25)
3.43 (0.24)
1.92 (0.72)
1.97 (0.81)
1.91 (0.96)
2.01 (0.77)
1.97 (0.96)
18.5 (0.07)
18.4 (0.05)
18.5 (0.07)
18.3 (0.05)
18.4 (0.06)
122 (8.75)
123 (8.06)
123 (5.78)
125 (9.25)
126 (8.66)
All values are for the average artifact-free 30-sec epoch of non–rapid eye movement stages 2– 4 sleep. N 5 16. PAA zero cross measures: BLX, no. of half-waves; TIM, time (sec) occupied by waves in frequency band;
IAM, integrated amplitude (mV z sec) in frequency band; CUL, peak-trough amplitude (mV) of waves in frequency band; ASA, average sample amplitude (mV) of waves in frequency band; FRQ, mean frequency (Hz). POW,
FFT-measured power (mV2 z sec). PAA zero derivative measures: DZX, half-waves; DTM, time; DCL, curve length; DFQ, mean frequency. Units are the same as those of zero cross measures.
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Table 3. Means (SEs) of Individual Differences across Successive Nights for Period Amplitude Analysis (PAA) Zero Cross and Zero Derivative Measures
and Fast-Fourier Transform (FFT) Power
BLX
TIM
IAM
CUL
ASA
FRQ
POW
Delta (0.3–3 Hz)
N1–N2
N2–N3
N3–N4
N4 –N5
Mean of 5 nights
DZX
DTM
DCL
DFQ
1.15 (0.21)
1.48 (0.31)
1.95 (0.49)
1.62 (0.49)
50.1 (0.99)
0.49 (0.09)
0.64 (0.14)
0.71 (0.19)
0.61 (0.16)
15.0 (0.38)
24.4 (4.97)
27.9 (4.99)
34.1 (5.85)
25.6 (4.93)
350.0 (16.5)
0.57 (0.11)
0.77 (0.14)
0.81 (0.14)
0.40 (0.07)
7.20 (0.26)
1.23 (0.23)
1.58 (0.25)
1.94 (0.33)
1.32 (0.20)
23.3 (0.78)
0.03 (0.01)
0.03 (0.01)
0.03 (0.01)
0.03 (0.01)
1.68 (0.02)
20.9 (3.94)
20.0 (4.31)
25.4 (6.15)
22.4 (4.33)
160.0 (12.8)
Sigma (12–15 Hz)
N1–N2
N2–N3
N3–N4
N4 –N5
Mean of 5 nights
1.83 (0.52)
1.72 (0.31)
2.00 (0.50)
1.51 (0.39)
27.3 (1.42)
0.07 (0.02)
0.06 (0.01)
0.07 (0.02)
0.06 (0.01)
1.02 (0.05)
0.69 (0.18)
0.64 (0.16)
0.67 (0.23)
0.61 (0.20)
6.55 (0.05)
0.08 (0.02)
0.08 (0.02)
0.08 (0.03)
0.08 (0.03)
0.65 (0.53)
0.40 (0.08)
0.43 (0.09)
0.36 (0.09)
0.35 (0.09)
6.29 (0.26)
0.02 (0.01)
0.02 (0.01)
0.03 (0.01)
0.02 (0.01)
13.4 (0.01)
0.23 (0.04)
0.27 (0.04)
0.30 (0.06)
0.31 (0.06)
3.10 (0.39)
5.05 (1.53)
6.03 (1.26)
6.32 (1.22)
3.12 (0.58)
81.0 (3.93)
0.20 (0.06)
0.23 (0.05)
0.24 (0.05)
0.12 (0.02)
3.06 (0.15)
0.12 (0.03)
0.19 (0.04)
0.20 (0.04)
0.11 (0.02)
1.72 (0.13)
0.07 (0.03)
0.09 (0.03)
0.12 (0.03)
0.08 (0.02)
13.3 (0.02)
Beta (15–23 Hz)
N1–N2
N2–N3
N3–N4
N4 –N5
Mean of 5 nights
2.63 (0.60)
2.82 (0.66)
3.13 (1.06)
3.25 (0.97)
46.2 (2.34)
0.07 (0.02)
0.08 (0.02)
0.09 (0.03)
0.09 (0.03)
1.25 (0.06)
0.87 (0.21)
0.87 (0.22)
0.81 (0.30)
0.77 (0.29)
6.02 (0.48)
0.14 (0.04)
0.15 (0.03)
0.12 (0.05)
0.13 (0.05)
0.81 (0.07)
0.46 (0.10)
0.49 (0.10)
0.30 (0.08)
0.29 (0.08)
4.72 (0.19)
0.03 (0.01)
0.03 (0.01)
0.04 (0.01)
0.02 (0.01)
18.5 (0.03)
0.21 (0.06)
0.29 (0.07)
0.18 (0.06)
0.19 (0.05)
1.41 (0.09)
10.5 (2.77)
18.1 (4.17)
19.8 (4.51)
11.1 (2.59)
124.0 (7.33)
0.27 (0.08)
0.47 (0.11)
0.52 (0.12)
0.29 (0.07)
3.36 (0.20)
0.29 (0.07)
0.40 (0.09)
0.41 (0.11)
0.26 (0.09)
1.96 (0.17)
0.13 (0.04)
0.15 (0.04)
0.18 (0.06)
0.15 (0.04)
18.4 (0.05)
All values are for the average artifact-free 30-sec epoch of non–rapid eye movement stages 2– 4 sleep. N 5 16. PAA zero cross measures: BLX, no. of half-waves; TIM, time (sec) occupied by waves in frequency band;
IAM, integrated amplitude (mV z sec) in frequency band; CUL, peak-trough amplitude (mV) of waves in frequency band; ASA, average sample amplitude (mV) of waves in frequency band; FRQ, mean frequency (Hz). POW,
FFT-measured power (mV2 z sec). PAA zero derivative measures: DZX, half-waves; DTM, time; DCL, curve length; DFQ, mean frequency. Units are the same as those of zero cross measures.
X. Tan et al
Internight Reliability of Sleep EEG
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Figure 1. (A) Mean 6 SE delta integrated amplitude (IAM) with
period amplitude analysis for each of 5 baseline nights. Also
shown are the delta IAM for the subject (S 4) with the highest
IAM and the subject (S 12) with the lowest IAM. (B) Delta
fast-Fourier transform power in the same format as for IAM. ●,
mean; Œ, S 4; , S 12.
Figure 2. (A) Mean 6 SE sigma derivative curve length (DCL)
with period amplitude analysis for each of 5 baseline nights. Also
shown are the sigma DCL for the subject (S 14) with the highest
DCL and the subject (S 12) with the lowest DCL. ●, mean; Œ, S
14; , S 12. (B) Sigma fast-Fourier transform power in the same
format as for DCL. ●, mean; Œ, S 13; , S 1.
experimental conditions, the reliability coefficients were
high and similar to those found here for the same measures. In addition, these two studies demonstrated excellent reproducibility of the absolute delta values. We have
been unable to find a systematic study of the reliability of
FFT-measured sleep EEG; however, there are two studies
that indirectly point to high reliability of NREM delta
power. Larsen et al (1995) reported high reliability across
2 nights for computer-scored stage 4. Since these scores
were based on FFT-measured delta (0.5– 4 Hz), they
indicate that the computer measurements were themselves
reliable across the 2 nights. Preud’homme and coworkers
(1997) demonstrated that the decline in NREM delta
(0.5–3 Hz) power across NREM periods on three successive baseline nights was highly stable with both linear and
exponential regressions. These observations suggest (but
do not establish) that FFT power in each NREM period
was also stable across these nights.
An unexpected result in Table 4 was that internight
correlation coefficients for zero cross measures of sigma
and beta were about as high as those for the zero derivative
measures. This result was unexpected because, as noted
above, one would expect zero derivative analysis to
estimate fast EEG more efficiently than zero cross methods because these faster waves are frequently superimposed on slower EEG. This result may be encouraging to
those who use only zero cross PAA. It was also surprising
that the zero cross correlations for mean frequency in both
sigma and beta were consistently higher than the zero
derivative correlations. The basis for this difference is not
immediately obvious.
Mean frequency correlations were generally substantially lower than those for amplitude. This may be due to
the fact that the average nightly difference per subject in
mean frequency was extremely small (Table 3). By this
measure, mean frequency measured within subjects was
remarkably stable. We suggest that the internight correlations were relatively low because the extremely narrow
spread of the frequency data allowed measurement error to
exert a proportionately greater effect on subjects’ rank-
1016
X. Tan et al
BIOL PSYCHIATRY
2000;48:1010 –1019
Table 4. Product Moment Correlations across Successive Baseline nights for Period Amplitude Analysis (PAA) Zero Cross and
Zero Derivative Measures and Fast-Fourier Transform (FFT) Power
Delta (0.3–3 Hz)
N1 vs. N2
N2 vs. N3
N3 vs. N4
N4 vs. N5
Median
Sigma (12–15 Hz)
N1 vs. N2
N2 vs. N3
N3 vs. N4
N4 vs. N5
Median
Beta (15–23 Hz)
N1 vs. N2
N2 vs. N3
N3 vs. N4
N4 vs. N5
Median
BLX
TIM
IAM
CUL
ASA
FRQ
POW
DZX
DTM
DCL
DFQ
.941
.902
.844
.863
.883
.930
.861
.826
.878
.870
.903
.891
.837
.869
.880
.826
.712
.669
.902
.769
.893
.869
.788
.868
.869
.890
.834
.828
.889
.862
.884
.905
.821
.829
.857
.898
.947
.883
.932
.915
.900
.949
.886
.933
.917
.904
.933
.877
.905
.905
.855
.902
.847
.860
.858
.897
.875
.899
.905
.898
.443
.733
.658
.851
.696
.985
.982
.971
.965
.977
.902
.895
.868
.976
.899
.892
.891
.861
.975
.892
.965
.912
.888
.966
.939
2.093a
.485
.342
.488
.414
.941
.940
.888
.889
.915
.943
.941
.888
.892
.917
.837
.844
.846
.859
.845
.820
.823
.849
.865
.836
.711
.698
.876
.891
.794
.824
.919
.937
.973
.928
.628
.484
.784
.815
.706
.896
.634
.680
.910
.788
.887
.664
.713
.920
.800
.859
.731
.784
.883
.822
.651
.650
.458
.546
.598
All values are for the average artifact-free 30-sec epoch of non–rapid eye movement stages 2– 4 sleep. N 5 16; r 5 .482, p , .05; r 5 .606, p , .01. PAA zero cross
measures: BLX, no. of half-waves; TIM, time (sec) occupied by waves in frequency band; IAM, integrated amplitude (mV z sec) in frequency band; CUL, peak-trough
amplitude (mV) of waves in frequency band; ASA, average sample amplitude (mV) of waves in frequency band; FRQ, mean frequency (Hz). POW, FFT-measured power
(mV2 z sec). PAA zero derivative measures: DZX, half-waves; DTM, time; DCL, curve length; DFQ, mean frequency. Units are the same as those of zero cross measures.
a
Low correlation due to single outlying point; with this point removed, r 5 .787.
Figure 3. Scattergram for night 2 vs. night 1 values of delta (A)
integrated amplitude (IAM) and (B) power. The high correlations
do not depend on outlying points.
Figure 4. Scattergram for night 2 vs. night 1 values of sigma (A)
derivative curve length (DCL) and (B) power. The high correlations do not depend on outlying points.
Internight Reliability of Sleep EEG
BIOL PSYCHIATRY
2000;48:1010 –1019
Figure 5. Scattergram for night 2 vs. night 1 values of beta (A)
derivative curve length (DCL) and (B) power. The high correlations do not depend on outlying points.
ings. This could lower the correlation coefficients, even
though the absolute values were quite close, as shown in
Table 3.
1017
The overall internight reliability of delta and sigma
measures (apart from sigma mean frequency) is remarkably high. Although beta also shows highly significant
internight reliability, its internight correlation coefficients
are consistently lower than those of delta and sigma. This
difference holds for both PAA and PSA measures. It is not
immediately obvious whether the somewhat lower correlations for beta are due to greater biological variability or
greater measurement error for this frequency band. This
result may also have been influenced by the fact that the
beta band employed was much wider (9 Hz) than those of
delta (2.7 Hz) and sigma (4 Hz).
With respect to the practical question of the number of
nights required to establish an adequate experimental
baseline for the different frequency bands, our data indicate that for delta and sigma power and their PAA
equivalents a single night provides a sufficiently high
correlation with the 5-night mean for most studies. For
other PAA measures of amplitude and incidence in delta
and sigma, 2 nights improve the correlation with the
5-night mean. For both PAA and PSA measures of beta, 2
baseline nights appear to be required and sufficient.
It has long been obvious that there are wide and
consistent individual differences in the amplitude of human EEGs. Factors that might produce these differences
include variations in skull impedance, volume conduction,
and brain size. These differences raise the question of
whether the high reliabilities of delta, sigma, and beta
power are wholly determined by individual differences in
amplitude. Period amplitude analysis but not PSA can
address this question. Period amplitude analysis demonstrates that wave incidence and period, as well as wave
Table 5. Correlations with the 5-Night Mean for Night 1, the Means of Nights 1 and 2, the Means of Nights 1–3, etc. for Period
Amplitude Analysis (PAA) Zero Cross and Zero Derivative Measures and Fast-Fourier Transform (FFT) Power
Delta (0.3–3 Hz)
N1 vs. mean
Ave (N1 1 N2) vs. mean
Ave (N1 1 N2 1 N3) vs. mean
Ave (N1 1 N2 1 N3 1 N4) vs. mean
Sigma (12–15 Hz)
N1 vs. mean
Ave (N1 1 N2) vs. mean
Ave (N1 1 N2 1 N3) vs. mean
Ave (N1 1 N2 1 N3 1 N4) vs. mean
Beta (15–23 Hz)
N1 vs. mean
Ave (N1 1 N2) vs. mean
Ave (N1 1 N2 1 N3) vs. mean
Ave (N1 1 N2 1 N3 1 N4) vs. mean
BLX
TIM
IAM
CUL
ASA
FRQ
POW
DZX
DTM
DCL
DFQ
.952
.972
.981
.994
.946
.972
.985
.995
.976
.986
.992
.997
.913
.955
.982
.996
.957
.982
.993
.998
.938
.968
.991
.998
.975
.989
.992
.997
.927
.978
.986
.998
.928
.979
.986
.998
.925
.975
.987
.998
.880
.964
.982
.997
.938
.971
.991
.998
.414
.838
.983
.979
.985
.992
.997
1.000
.888
.962
.984
.997
.880
.960
.983
.997
.953
.977
.990
.998
.414
.838
.938
.979
.945
.978
.987
.997
.945
.978
.987
.997
.855
.952
.981
.996
.835
.942
.977
.996
.828
.918
.980
.996
.824
.925
.973
.986
.801
.877
.979
.993
.861
.939
.976
.993
.851
.937
.976
.993
.870
.955
.984
.995
.824
.925
.973
.986
All values are for the average artifact-free 30-sec epoch of non–rapid eye movement stages 2– 4 sleep. N 5 16; r 5 .482, p , .05; r 5 .606, p , .01. PAA zero cross
measures: BLX, no. of half-waves; TIM, time (sec) occupied by waves in frequency band; IAM, integrated amplitude (mV z sec) in frequency band; CUL, peak-trough
amplitude (mV) of waves in frequency band; ASA, average sample amplitude (mV) of waves in frequency band; FRQ, mean frequency (Hz). POW, FFT-measured power
(mV2 z sec). PAA zero derivative measures: DZX, half-waves; DTM, time; DCL, curve length; DFQ, mean frequency. Units are the same as those of zero cross measures.
1018
X. Tan et al
BIOL PSYCHIATRY
2000;48:1010 –1019
amplitudes, are highly reliable in the three frequency
bands.
In a discussion of PAA versus PSA, Reynolds and
Brunner (1995) stated: “If wave amplitude and incidence
of EEG frequencies are expected to be differently affected
by mental illness or external perturbation (challenges)
PAA should be used.” But this begs the question of how
one could know in advance whether to expect differential
effects. Thus far, PAA has shown differential effects on
human sleep EEG of age (Feinberg et al 1981, 1990),
hypnotics (Feinberg et al 1979), naps (Feinberg et al 1985,
1992), and sleep deprivation (Feinberg et al 1987). In rats,
differential effects on EEG amplitude and incidence are
produced by sleep deprivation (Feinberg and Campbell
1993b), ambient light (Campbell and Feinberg 1993), and
N-methyl-D-aspartate receptor blockade (Feinberg and
Campbell 1993a). These observations indicate that PAA
should be included in any study where the differential
effects on the incidence and amplitude of EEG waves that
alter spectral power might be theoretically or clinically
important. The recent demonstration by Uchida et al
(1999) that the PAA zero derivative analysis gives good
agreement with FFT power in the faster frequencies adds
confidence to its use for measuring amplitude and incidence of fast EEG. It is efficient to employ software such
as PASS PLUS, which simultaneously applies validated
PAA methods and standard FFT analysis to the same
digitized data (for an example of the value of this
combination in rat sleep, see Campbell and Feinberg
1999).
The almost perfect internight correlations for FFT
power in the sigma band (median r 5 .977) merit
comment. Assuming that this is not a chance result, one
wonders whether these correlations are so high because
there are marked and stable individual differences in
organized spindle activity. Uchida et al (1991) suggested
that spindles in normal young adults probably dominate
FFT power in the sigma band, a hypothesis verified
experimentally by Dijk et al (1993). Another factor contributing to the high reliability of spindles might be that
they are particularly well suited for PSA measurement
because their waveforms more closely approximate the
Fourier assumptions of sinusoidal shape and (within our
;5-sec epoch length) stationarity.
One question raised by our findings is whether the
stability of the NREM delta EEG across nights is consistent with its postulated role as a marker of sleep homeostasis. According to the homeostatic model as initially
formulated (Feinberg 1974), NREM delta is a correlate of
a process by which the brain reverses the “neurometabolic” effects of plastic waking processes. The “two
process” homeostatic model (Borbely 1982) makes a
similar inference, although less explicitly. In this experi-
ment we did not control subjects’ daytime activities, apart
from time awake; however, it was anecdotally apparent
that these activities could vary considerably over the 5
days of study. At times, subjects studied intensively for
examinations until just before bedtime, and on other days
they relaxed and watched television. Their physical activities and participation in sports also varied from day to
day. Nevertheless, delta was stable across nights and
internight correlations were consistently high. This finding
suggests that normal variations in daytime behavior of
college students have little effect on NREM delta (or
sigma and beta) so long as wake time is controlled. This
result is more consistent with “traitlike” than “statelike”
behavior.
This work was supported by a University of California, Davis Faculty
Research Award (IF); by Lorex Pharmaceuticals; by the Department of
Veterans Affairs; and by U.S. Public Health Service Grants No.
R01MH50741 and No. R01MH57928.
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NREM Delta, Sigma, and Beta: Biological Implications
Xin Tan, Ian Glenn Campbell, Laura Palagini, and Irwin Feinberg
Background: Computer analysis of the sleep electroencephalogram (EEG) waveforms is widely employed, but
there have been no systematic studies of its reliability.
Methods: The most commonly used computer methods are
power spectral analysis with the fast-Fourier transform
(FFT) and period amplitude analysis (PAA) with zero
cross or zero first derivative half-wave measurement. We
applied all three computer methods to the digitized EEG of
16 normal subjects who underwent 5 consecutive nights of
baseline (placebo) recording. We evaluated the internight
reliability of three non–rapid eye movement (NREM)
frequency bands of special importance to sleep research:
delta (0.3–3 Hz), sigma (12–15 Hz), and beta (15–23 Hz).
Results: Both FFT and the two methods of PAA gave
excellent internight reliability for delta and sigma. Even a
single night of recording correlated highly (r . .9) with
the 5-night mean. Beta reliability was lower but still highly
significant for both the PAA and the FFT measures.
Conclusions: Computer-analyzed sleep EEG data are
highly reliable. Period amplitude methods demonstrate
that wave incidence and period as well as amplitude are
reliable, indicating that the reliability of composite measures (FFT power, PAA integrated amplitude) is not solely
based on individual differences in EEG amplitude. The
high internight stability of NREM delta indicates that it
possesses traitlike characteristics and is relatively independent of day-to-day variations in state. Biol Psychiatry 2000;48:1010 –1019 © 2000 Society of Biological
Psychiatry
Key Words:
homeostasis
Sleep
EEG,
computer,
reliability,
Introduction
T
he value of computer measurement of sleep electroencephalogram (EEG) waveforms is now generally
accepted. The two most widely employed methods are
period amplitude analysis (PAA) with zero cross and zero
derivative algorithms, and power spectral analysis (PSA)
with the fast-Fourier transform (FFT). In spite of increasing use of these methods, there are few published data on
their internight reliability or on the absolute magnitudes of
the differences that occur across baseline nights. Such data
could help formulate experimental designs, such as including estimates of experimental power and decisions on the
number of baseline nights to record. Knowledge of the
absolute magnitudes of internight variation under baseline
conditions could also be useful for evaluating experimental effects reported in the literature.
We are currently carrying out a large-scale investigation
of the reliability of computer-measured sleep EEG under
baseline conditions in young adults. Our analyses include
a wide frequency range (0 –100 Hz) in both non–rapid eye
movement (NREM) and REM sleep. Here we present
initial results for three NREM frequency bands of particular interest for the biology of sleep: delta (0.3–3 Hz),
sigma (12–15 Hz), and beta (15–23 Hz). The delta band is
of interest because of its close relation to maturation and
aging (Feinberg et al 1967, 1981, 1990; Williams et al
1974) over the human life span and its central role in
homeostatic models (Borbely 1982; Feinberg 1974).
Sigma and beta are also of considerable interest. Organized spindles in 12–15 Hz are a distinguishing hallmark
of the NREM EEG. Next to delta, organized spindles are
the waveforms that show greatest decline between young
adulthood and normal old age (Guazzelli et al 1986). It is
of further biological interest that sigma and beta power
exhibit systematic dynamic relations to delta, relations that
have spurred considerable research interest (Aeschbach et
al 1997; Borbely 1998) since their initial descriptions by
Uchida et al (1991).
Methods and Materials
Subjects
From the Department of Psychiatry, University of California, Davis (XT, IGC, IF),
Psychiatry Clinic, University of Pisa, Pisa, Italy (LP), and Veterans Administration Northern California Health Care System, Martinez (IF).
Address reprint requests to Irwin Feinberg, M.D., University of California,
VA/UCD Sleep Lab TB 148, Davis CA 95616.
Received October 18, 1999; revised February 22, 2000; accepted March 2, 2000.
© 2000 Society of Biological Psychiatry
The data for these analyses were obtained in a study that
compared the sleep EEG effects of three g-aminobutyric acid–
ergic hypnotics to placebo. The drugs were zolpidem (10 mg),
triazolam (0.25 mg), and temazepam (15 mg), given a half hour
before sleep in capsules identical to placebo. Preliminary reports
0006-3223/00/$20.00
PII S0006-3223(00)00873-8
Internight Reliability of Sleep EEG
of the drug effects on NREM delta, sigma, and beta frequencies
have been presented (Feinberg et al 1995a, 1995b). The study
was comprised of four treatment arms with sleep laboratory
recording on 5 consecutive nights. In each arm, subjects received
one of the active drugs or placebo for the first 3 nights and then
placebo for the final 2 nights. Therefore, in one treatment arm,
subjects received placebo for 5 consecutive nights. These placebo data were used for the reliability analyses. Subjects were
students at University of California, Davis who gave informed
consent and were paid for their participation. There were 10 male
and six female subjects between the ages of 19 and 26 years
(mean 5 20.1, SD 5 2.5). All were nonsmokers, within 25% of
the desirable weight for their height (according to the Metropolitan Life Insurance Table), and in excellent health according to
medical and psychiatric evaluations and a laboratory screen. No
subject used alcohol or other drugs of abuse during the study, and
urine drug screens were routinely performed. Time in bed was
meticulously controlled, with subjects in bed from 11:30 PM to
7:00 AM on each recording night and for the 3 nights at home that
preceded the laboratory recordings. Daytime naps were prohibited. The protocol required 2 nights of polygraphic screening to
rule out apnea and myoclonus and to establish that subjects had
normal sleep latencies (SLs), total sleep time (TST), and stages
3– 4 sleep. Criteria for acceptance were SL mean for 2 nights ,
20 min, TST mean for 2 nights of at least 400 min out of the
450-min recording period, and combined stages 3– 4 $ 15%
TST.
Recording and Calibration
The C3-A2 EEG was recorded continuously with a Grass
(Quincy, MA) Model 78 polygraph. A half-amplitude lowfrequency filter was set at 0.3 Hz, and a high frequency filter at
0.1 kHz. The preamplifier output was digitized at 200 Hz. The
digitized values were saved to optical disk and analyzed with
PASS PLUS (Delta Software, St. Louis). Of particular importance for quantitative EEG studies is careful calibration. PASS
PLUS analyzed a calibrated 3.5-Hz, 200-mV peak-to-peak sine
wave before each night’s recording and scaled the PAA and PSA
measurements on each channel to this standard.
Analyses of Sleep EEG
Visual scoring was performed on the ink-written record as
required by the research protocol. The scoring was done on
30-sec epochs without knowledge of drug condition (“blind”) by
two raters, with discrepancies resolved by a third rater. Rechtschaffen and Kales (1968) criteria for sleep stages were
applied. Movement and other artifacts were also scored. Correspondence between the visual scoring of the ink-written record
and the computer measures was accomplished with a computergenerated time code written on the polygraph record every 10 sec
by the digital-to-analog converter (with PASS PLUS on-screen
scoring, this correspondence is automatic). The computer data
reported below are based on all visually categorized, artifact-free
epochs of NREM scored as stages 2– 4 on each baseline
(placebo) night. There were, on average, 598 NREM (stages
2– 4) epochs per night.
BIOL PSYCHIATRY
2000;48:1010 –1019
1011
Table 1. Period Amplitude Analysis Measures and Their
Definitions
Zero cross measures for each frequency band
Number of half-waves (BLX; measured as number of baseline
(zero) crossings)
Time in band (TIM; sum of all half-wave durations, measured in sec)
Integrated amplitude (IAM; sum of all half-wave integrated amplitudes,
in mV z sec)
Curve length (CUL; sum of all half-wave, peak-trough amplitudes, in
mV)
Average sample amplitude (ASA; IAM/TIM, in mV)
Mean frequency (FRQ; [BLX/2]/TIM, in Hz)
Zero first derivative measures for each frequency band
Derivative half-wave count (DZX; the number of zero derivative halfwaves)
Derivative time in band (DTM; sum of all derivative half-wave
durations, in sec)
Derivative curve length (DCL; sum of all peak-trough voltage
differences, in mV)
Derivative frequency (DFQ; [DZX/2]/DTM, in Hz)
Computer Analyses
PAA WITH PASS PLUS. Two methods of detection and
measurement are simultaneously applied by PASS PLUS period
analysis: half-wave detection by successive crossings of zero
voltage and by successive zero first derivative points. Zero cross
(also called baseline crossing) analysis is more effective for slow
frequencies. Zero derivative analysis is more suitable for fast
EEG waves, which are often superimposed on slower activity
and do not cross zero voltage. The algorithms for both PAA
methods have been published, along with initial data on their
reliability and the reproducibility of the absolute values obtained
in similar groups (Feinberg et al 1978, 1980). Linear interpolation has been incorporated in PASS PLUS PAA algorithms since
their inception. Such interpolation greatly improves resolution of
wave periods (frequencies) without the processing and storage
costs of high sample rates (“oversampling,” J.D. March, unpublished manuscript). Both the zero cross and the zero derivative
PAA yield separate estimates for wave number, period, and
amplitude. From these, several biologically meaningful ratios
(e.g. amplitude/half-wave, mean frequency) can be computed.
Since most laboratories that use PAA apply only zero cross
algorithms, we report here the internight reliability data for zero
cross measures for sigma and beta as well as for delta. (Normally
our laboratory uses zero cross measures for delta and zero
derivative measures for all higher frequencies, including sigma
and beta.) The specific PAA measures and their definitions are
shown in Table 1 (for details, see Feinberg et al 1978).
PSA WITH PASS PLUS. Fast-Fourier transform was performed on 30-sec epochs of 5.120-sec Welch tapered windows
with 2.620-sec overlap. This yielded 12 windows per 30-sec
epoch. The bands used in the analyses here were 0.3–3 Hz for
delta, 12–15 Hz for sigma, and 15–23 Hz for beta. (The actual
frequency bands differ slightly from these nominal values: delta
is 0.29 –3.03; sigma is 12.01–14.94, and beta is 14.94 –22.95).
Fast-Fourier transform analysis yielded the classical measure
of power in mV2 z sec for each frequency band.
1012
X. Tan et al
BIOL PSYCHIATRY
2000;48:1010 –1019
Results
Table 2 lists the group means and SEs for each night for
each computer measure. These means were remarkably
stable, being virtually identical on each of the 5 nights for
both PAA and PSA measures; however, stability of group
means across nights can mask large individual variations
because subjects whose values are higher on one night
could be offset by subjects whose values vary in the
opposite direction. Table 3, which presents the average
within-subject difference across successive nights, shows
that this did not occur. Night–to-night individual differences for each measure were quite small. There was no
trend for mean internight differences per subject to increase or decrease across the 5 consecutive baseline
nights.
Figures 1 and 2 plot the values across 5 baseline nights
for PAA integrated amplitude and FFT power in the delta
and sigma bands for the subjects with the highest and
lowest 5-night means. These subjects were chosen because
they might be expected to show the greatest variation
because of the tendency toward regression to the mean. In
fact, the values for even these extreme subjects were quite
stable across the 5 nights.
As would be expected from the remarkable stability of
the group means and the small within-subject differences
across nights, the reliability of these computer-measured
EEG frequencies as estimated by Pearson correlation
coefficients was quite high. These data are shown in Table
4, which presents the correlation coefficients for each
measure across successive nights for delta, sigma, and beta
computed for the average 30-sec epoch of NREM sleep.
The five zero cross measures of delta showed consistently
high internight correlations. There was no tendency for
their correlation coefficients to increase or decrease across
successive nights. Delta power with PSA showed a median
internight correlation of .857, quite close to the .880 for
delta integrated amplitude, the similar PAA measure.
Table 4 shows the internight reliability for sigma.
Although our laboratory uses zero derivative PAA measures for sigma, we also present the zero cross results,
since most laboratories that use PAA apply only zero cross
measurement. With the exception of derivative mean
frequency, which had low and statistically insignificant
correlations, both the zero cross and the zero derivative
measures for sigma showed high internight correlations.
The correlations for sigma FFT power, although not
significantly greater than those for the corresponding PAA
measures, were notably high, with a median of .977 and a
narrow range of .965–.985.
Table 4 also shows that internight correlations for PAA
measures of NREM beta EEG were also substantial.
Somewhat surprisingly, the reliability for the beta zero
cross measures was about equal to that of the zero
derivative measures. The median correlation of .706 for
beta power with PSA was somewhat lower than its PAA
equivalent (.822), although this difference was not statistically significant.
Figures 3–5 present night 1–night 2 scattergrams for
integrated amplitude (PAA zero cross) and FFT power for
delta, and derivative curve length (PAA zero first derivative) and FFT power for sigma and beta bands. Nights 1
and 2 were chosen a priori because they might be
expected to have the poorest correlations because of
readaptation effects, and thereby more strongly challenge
the reliability of the computer measures. Figures 3–5
demonstrate strong linear relationships and also show that
the correlation coefficients do not depend upon outlying
points. We also plotted scattergrams for all other correlations, and none depended upon outlying points. However,
a markedly aberrant point dominated the night 1–night 2
correlation for derivative mean frequency, reducing its
correlation coefficient to near zero (Table 4). With this
point removed, the correlation increased from 2.093 to
.787.
In designing sleep experiments, one important decision
is the number of baseline nights to record. We assumed
that, for most experiments, the largest practical number
would be 5. We therefore tested the number of baseline
nights required to obtain high correlations with the 5-night
mean. These results are shown in Table 5. For delta and
sigma FFT power and their PAA equivalents, any single
night, including the first, provided a high correlation with
the 5-night mean that was not appreciably increased by
adding additional nights. For PAA measures of amplitude,
incidence, and period, 2 baseline nights appeared to
improve the correlation. Beta waveform measures appeared to require 2–3 nights for adequate correlations with
the 5-night mean with both PSA and PAA.
Discussion
There are few published studies to which we can compare
our findings. The most extensive previous reliability
analyses of PAA zero cross measures were reported when
we described the PAA algorithms (Feinberg et al 1978,
1980). In the first study, correlation coefficients for delta
zero cross measures across 2 baseline nights for the
average 20-sec epoch of NREM were high, ranging from
a low of .86 to a high of .91 (N 5 20). It was presumably
this high reliability that allowed us to detect significant
correlations of delta measures with age, even within the
narrow range of 18 –23 years. In the second study, the first
four cycles of an extended night and a recovery night were
compared for zero cross measures up to 23 Hz. Despite the
fact that correlations were computed across different
Internight Reliability of Sleep EEG
Table 2. Means (SEs) for Each of 5 Consecutive Baseline (Placebo) Nights for Period Amplitude Analysis (PAA) Zero Cross and Zero Derivative Measures
and Fast-Fourier Transform (FFT) Power
BLX
TIM
IAM
CUL
ASA
FRQ
POW
Delta (0.3–3 Hz)
N1
N2
N3
N4
N5
49.5 (1.0)
49.8 (1.1)
50.5 (0.9)
50.5 (1.3)
50.2 (1.1)
14.8 (0.40)
14.9 (0.40)
15.2 (0.35)
15.1 (0.46)
15.0 (0.39)
343 (18.1)
349 (17.5)
352 (19.2)
355 (15.9)
349 (15.5)
6.96 (0.27)
7.25 (0.28)
7.23 (0.34)
7.33 (0.26)
7.23 (0.28)
23.1 (0.85)
23.3 (0.83)
23.1 (0.95)
23.6 (0.74)
23.2 (0.75)
1.68 (0.02)
1.67 (0.02)
1.67 (0.02)
1.68 (0.02)
1.68 (0.02)
156 (13.9)
160 (13.4)
162 (15.7)
165 (12.3)
159 (11.8)
Sigma (12–15 Hz)
N1
N2
N3
N4
N5
27.9 (1.53)
27.6 (1.56)
26.9 (1.37)
27.3 (1.51)
27.3 (1.47)
1.04 (0.06)
1.03 (0.06)
1.00 (0.05)
1.01 (0.06)
1.02 (0.06)
6.63 (0.55)
6.69 (0.58)
6.39 (0.51)
6.55 (0.59)
6.47 (0.53)
0.65 (0.05)
0.67 (0.06)
0.64 (0.05)
0.66 (0.07)
0.64 (0.05)
6.27 (0.26)
6.37 (0.29)
6.27 (0.26)
6.30 (0.29)
6.25 (0.27)
13.43 (0.01)
13.44 (0.01)
13.43 (0.01)
13.45 (0.01)
13.44 (0.01)
3.11 (0.40)
3.13 (0.42)
3.04 (0.40)
3.11 (0.38)
3.12 (0.37)
Beta (15–23 Hz)
N1
N2
N3
N4
N5
47.1 (2.61)
46.5 (2.57)
45.5 (2.05)
45.7 (2.76)
46.1 (2.25)
1.27 (0.07)
1.26 (0.07)
1.23 (0.06)
1.24 (0.08)
1.25 (0.06)
6.13 (0.51)
6.17 (0.56)
5.87 (0.42)
6.03 (0.64)
5.91 (0.44)
0.82 (0.08)
0.83 (0.09)
0.79 (0.06)
0.81 (0.10)
0.79 (0.06)
4.74 (0.19)
4.79 (0.21)
4.70 (0.20)
4.73 (0.23)
4.66 (0.20)
18.47 (0.02)
18.47 (0.02)
18.46 (0.03)
18.46 (0.03)
18.46 (0.03)
1.40 (0.09)
1.43 (0.08)
1.37 (0.11)
1.43 (0.12)
1.40 (0.11)
DZX
DTM
DCL
DFQ
82.7 (4.25)
80.6 (4.47)
80.4 (3.77)
80.2 (4.07)
81.4 (4.23)
3.12 (0.16)
3.04 (0.17)
3.04 (0.14)
3.01 (0.15)
3.07 (0.16)
1.74 (0.80)
1.72 (0.94)
1.69 (0.91)
1.72 (0.89)
1.72 (0.96)
13.24 (0.03)
13.26 (0.02)
13.23 (0.04)
13.30 (0.02)
13.26 (0.03)
3.31 (0.23)
3.33 (0.22)
3.32 (0.16)
3.41 (0.25)
3.43 (0.24)
1.92 (0.72)
1.97 (0.81)
1.91 (0.96)
2.01 (0.77)
1.97 (0.96)
18.5 (0.07)
18.4 (0.05)
18.5 (0.07)
18.3 (0.05)
18.4 (0.06)
122 (8.75)
123 (8.06)
123 (5.78)
125 (9.25)
126 (8.66)
All values are for the average artifact-free 30-sec epoch of non–rapid eye movement stages 2– 4 sleep. N 5 16. PAA zero cross measures: BLX, no. of half-waves; TIM, time (sec) occupied by waves in frequency band;
IAM, integrated amplitude (mV z sec) in frequency band; CUL, peak-trough amplitude (mV) of waves in frequency band; ASA, average sample amplitude (mV) of waves in frequency band; FRQ, mean frequency (Hz). POW,
FFT-measured power (mV2 z sec). PAA zero derivative measures: DZX, half-waves; DTM, time; DCL, curve length; DFQ, mean frequency. Units are the same as those of zero cross measures.
BIOL PSYCHIATRY
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Table 3. Means (SEs) of Individual Differences across Successive Nights for Period Amplitude Analysis (PAA) Zero Cross and Zero Derivative Measures
and Fast-Fourier Transform (FFT) Power
BLX
TIM
IAM
CUL
ASA
FRQ
POW
Delta (0.3–3 Hz)
N1–N2
N2–N3
N3–N4
N4 –N5
Mean of 5 nights
DZX
DTM
DCL
DFQ
1.15 (0.21)
1.48 (0.31)
1.95 (0.49)
1.62 (0.49)
50.1 (0.99)
0.49 (0.09)
0.64 (0.14)
0.71 (0.19)
0.61 (0.16)
15.0 (0.38)
24.4 (4.97)
27.9 (4.99)
34.1 (5.85)
25.6 (4.93)
350.0 (16.5)
0.57 (0.11)
0.77 (0.14)
0.81 (0.14)
0.40 (0.07)
7.20 (0.26)
1.23 (0.23)
1.58 (0.25)
1.94 (0.33)
1.32 (0.20)
23.3 (0.78)
0.03 (0.01)
0.03 (0.01)
0.03 (0.01)
0.03 (0.01)
1.68 (0.02)
20.9 (3.94)
20.0 (4.31)
25.4 (6.15)
22.4 (4.33)
160.0 (12.8)
Sigma (12–15 Hz)
N1–N2
N2–N3
N3–N4
N4 –N5
Mean of 5 nights
1.83 (0.52)
1.72 (0.31)
2.00 (0.50)
1.51 (0.39)
27.3 (1.42)
0.07 (0.02)
0.06 (0.01)
0.07 (0.02)
0.06 (0.01)
1.02 (0.05)
0.69 (0.18)
0.64 (0.16)
0.67 (0.23)
0.61 (0.20)
6.55 (0.05)
0.08 (0.02)
0.08 (0.02)
0.08 (0.03)
0.08 (0.03)
0.65 (0.53)
0.40 (0.08)
0.43 (0.09)
0.36 (0.09)
0.35 (0.09)
6.29 (0.26)
0.02 (0.01)
0.02 (0.01)
0.03 (0.01)
0.02 (0.01)
13.4 (0.01)
0.23 (0.04)
0.27 (0.04)
0.30 (0.06)
0.31 (0.06)
3.10 (0.39)
5.05 (1.53)
6.03 (1.26)
6.32 (1.22)
3.12 (0.58)
81.0 (3.93)
0.20 (0.06)
0.23 (0.05)
0.24 (0.05)
0.12 (0.02)
3.06 (0.15)
0.12 (0.03)
0.19 (0.04)
0.20 (0.04)
0.11 (0.02)
1.72 (0.13)
0.07 (0.03)
0.09 (0.03)
0.12 (0.03)
0.08 (0.02)
13.3 (0.02)
Beta (15–23 Hz)
N1–N2
N2–N3
N3–N4
N4 –N5
Mean of 5 nights
2.63 (0.60)
2.82 (0.66)
3.13 (1.06)
3.25 (0.97)
46.2 (2.34)
0.07 (0.02)
0.08 (0.02)
0.09 (0.03)
0.09 (0.03)
1.25 (0.06)
0.87 (0.21)
0.87 (0.22)
0.81 (0.30)
0.77 (0.29)
6.02 (0.48)
0.14 (0.04)
0.15 (0.03)
0.12 (0.05)
0.13 (0.05)
0.81 (0.07)
0.46 (0.10)
0.49 (0.10)
0.30 (0.08)
0.29 (0.08)
4.72 (0.19)
0.03 (0.01)
0.03 (0.01)
0.04 (0.01)
0.02 (0.01)
18.5 (0.03)
0.21 (0.06)
0.29 (0.07)
0.18 (0.06)
0.19 (0.05)
1.41 (0.09)
10.5 (2.77)
18.1 (4.17)
19.8 (4.51)
11.1 (2.59)
124.0 (7.33)
0.27 (0.08)
0.47 (0.11)
0.52 (0.12)
0.29 (0.07)
3.36 (0.20)
0.29 (0.07)
0.40 (0.09)
0.41 (0.11)
0.26 (0.09)
1.96 (0.17)
0.13 (0.04)
0.15 (0.04)
0.18 (0.06)
0.15 (0.04)
18.4 (0.05)
All values are for the average artifact-free 30-sec epoch of non–rapid eye movement stages 2– 4 sleep. N 5 16. PAA zero cross measures: BLX, no. of half-waves; TIM, time (sec) occupied by waves in frequency band;
IAM, integrated amplitude (mV z sec) in frequency band; CUL, peak-trough amplitude (mV) of waves in frequency band; ASA, average sample amplitude (mV) of waves in frequency band; FRQ, mean frequency (Hz). POW,
FFT-measured power (mV2 z sec). PAA zero derivative measures: DZX, half-waves; DTM, time; DCL, curve length; DFQ, mean frequency. Units are the same as those of zero cross measures.
X. Tan et al
Internight Reliability of Sleep EEG
BIOL PSYCHIATRY
2000;48:1010 –1019
1015
Figure 1. (A) Mean 6 SE delta integrated amplitude (IAM) with
period amplitude analysis for each of 5 baseline nights. Also
shown are the delta IAM for the subject (S 4) with the highest
IAM and the subject (S 12) with the lowest IAM. (B) Delta
fast-Fourier transform power in the same format as for IAM. ●,
mean; Œ, S 4; , S 12.
Figure 2. (A) Mean 6 SE sigma derivative curve length (DCL)
with period amplitude analysis for each of 5 baseline nights. Also
shown are the sigma DCL for the subject (S 14) with the highest
DCL and the subject (S 12) with the lowest DCL. ●, mean; Œ, S
14; , S 12. (B) Sigma fast-Fourier transform power in the same
format as for DCL. ●, mean; Œ, S 13; , S 1.
experimental conditions, the reliability coefficients were
high and similar to those found here for the same measures. In addition, these two studies demonstrated excellent reproducibility of the absolute delta values. We have
been unable to find a systematic study of the reliability of
FFT-measured sleep EEG; however, there are two studies
that indirectly point to high reliability of NREM delta
power. Larsen et al (1995) reported high reliability across
2 nights for computer-scored stage 4. Since these scores
were based on FFT-measured delta (0.5– 4 Hz), they
indicate that the computer measurements were themselves
reliable across the 2 nights. Preud’homme and coworkers
(1997) demonstrated that the decline in NREM delta
(0.5–3 Hz) power across NREM periods on three successive baseline nights was highly stable with both linear and
exponential regressions. These observations suggest (but
do not establish) that FFT power in each NREM period
was also stable across these nights.
An unexpected result in Table 4 was that internight
correlation coefficients for zero cross measures of sigma
and beta were about as high as those for the zero derivative
measures. This result was unexpected because, as noted
above, one would expect zero derivative analysis to
estimate fast EEG more efficiently than zero cross methods because these faster waves are frequently superimposed on slower EEG. This result may be encouraging to
those who use only zero cross PAA. It was also surprising
that the zero cross correlations for mean frequency in both
sigma and beta were consistently higher than the zero
derivative correlations. The basis for this difference is not
immediately obvious.
Mean frequency correlations were generally substantially lower than those for amplitude. This may be due to
the fact that the average nightly difference per subject in
mean frequency was extremely small (Table 3). By this
measure, mean frequency measured within subjects was
remarkably stable. We suggest that the internight correlations were relatively low because the extremely narrow
spread of the frequency data allowed measurement error to
exert a proportionately greater effect on subjects’ rank-
1016
X. Tan et al
BIOL PSYCHIATRY
2000;48:1010 –1019
Table 4. Product Moment Correlations across Successive Baseline nights for Period Amplitude Analysis (PAA) Zero Cross and
Zero Derivative Measures and Fast-Fourier Transform (FFT) Power
Delta (0.3–3 Hz)
N1 vs. N2
N2 vs. N3
N3 vs. N4
N4 vs. N5
Median
Sigma (12–15 Hz)
N1 vs. N2
N2 vs. N3
N3 vs. N4
N4 vs. N5
Median
Beta (15–23 Hz)
N1 vs. N2
N2 vs. N3
N3 vs. N4
N4 vs. N5
Median
BLX
TIM
IAM
CUL
ASA
FRQ
POW
DZX
DTM
DCL
DFQ
.941
.902
.844
.863
.883
.930
.861
.826
.878
.870
.903
.891
.837
.869
.880
.826
.712
.669
.902
.769
.893
.869
.788
.868
.869
.890
.834
.828
.889
.862
.884
.905
.821
.829
.857
.898
.947
.883
.932
.915
.900
.949
.886
.933
.917
.904
.933
.877
.905
.905
.855
.902
.847
.860
.858
.897
.875
.899
.905
.898
.443
.733
.658
.851
.696
.985
.982
.971
.965
.977
.902
.895
.868
.976
.899
.892
.891
.861
.975
.892
.965
.912
.888
.966
.939
2.093a
.485
.342
.488
.414
.941
.940
.888
.889
.915
.943
.941
.888
.892
.917
.837
.844
.846
.859
.845
.820
.823
.849
.865
.836
.711
.698
.876
.891
.794
.824
.919
.937
.973
.928
.628
.484
.784
.815
.706
.896
.634
.680
.910
.788
.887
.664
.713
.920
.800
.859
.731
.784
.883
.822
.651
.650
.458
.546
.598
All values are for the average artifact-free 30-sec epoch of non–rapid eye movement stages 2– 4 sleep. N 5 16; r 5 .482, p , .05; r 5 .606, p , .01. PAA zero cross
measures: BLX, no. of half-waves; TIM, time (sec) occupied by waves in frequency band; IAM, integrated amplitude (mV z sec) in frequency band; CUL, peak-trough
amplitude (mV) of waves in frequency band; ASA, average sample amplitude (mV) of waves in frequency band; FRQ, mean frequency (Hz). POW, FFT-measured power
(mV2 z sec). PAA zero derivative measures: DZX, half-waves; DTM, time; DCL, curve length; DFQ, mean frequency. Units are the same as those of zero cross measures.
a
Low correlation due to single outlying point; with this point removed, r 5 .787.
Figure 3. Scattergram for night 2 vs. night 1 values of delta (A)
integrated amplitude (IAM) and (B) power. The high correlations
do not depend on outlying points.
Figure 4. Scattergram for night 2 vs. night 1 values of sigma (A)
derivative curve length (DCL) and (B) power. The high correlations do not depend on outlying points.
Internight Reliability of Sleep EEG
BIOL PSYCHIATRY
2000;48:1010 –1019
Figure 5. Scattergram for night 2 vs. night 1 values of beta (A)
derivative curve length (DCL) and (B) power. The high correlations do not depend on outlying points.
ings. This could lower the correlation coefficients, even
though the absolute values were quite close, as shown in
Table 3.
1017
The overall internight reliability of delta and sigma
measures (apart from sigma mean frequency) is remarkably high. Although beta also shows highly significant
internight reliability, its internight correlation coefficients
are consistently lower than those of delta and sigma. This
difference holds for both PAA and PSA measures. It is not
immediately obvious whether the somewhat lower correlations for beta are due to greater biological variability or
greater measurement error for this frequency band. This
result may also have been influenced by the fact that the
beta band employed was much wider (9 Hz) than those of
delta (2.7 Hz) and sigma (4 Hz).
With respect to the practical question of the number of
nights required to establish an adequate experimental
baseline for the different frequency bands, our data indicate that for delta and sigma power and their PAA
equivalents a single night provides a sufficiently high
correlation with the 5-night mean for most studies. For
other PAA measures of amplitude and incidence in delta
and sigma, 2 nights improve the correlation with the
5-night mean. For both PAA and PSA measures of beta, 2
baseline nights appear to be required and sufficient.
It has long been obvious that there are wide and
consistent individual differences in the amplitude of human EEGs. Factors that might produce these differences
include variations in skull impedance, volume conduction,
and brain size. These differences raise the question of
whether the high reliabilities of delta, sigma, and beta
power are wholly determined by individual differences in
amplitude. Period amplitude analysis but not PSA can
address this question. Period amplitude analysis demonstrates that wave incidence and period, as well as wave
Table 5. Correlations with the 5-Night Mean for Night 1, the Means of Nights 1 and 2, the Means of Nights 1–3, etc. for Period
Amplitude Analysis (PAA) Zero Cross and Zero Derivative Measures and Fast-Fourier Transform (FFT) Power
Delta (0.3–3 Hz)
N1 vs. mean
Ave (N1 1 N2) vs. mean
Ave (N1 1 N2 1 N3) vs. mean
Ave (N1 1 N2 1 N3 1 N4) vs. mean
Sigma (12–15 Hz)
N1 vs. mean
Ave (N1 1 N2) vs. mean
Ave (N1 1 N2 1 N3) vs. mean
Ave (N1 1 N2 1 N3 1 N4) vs. mean
Beta (15–23 Hz)
N1 vs. mean
Ave (N1 1 N2) vs. mean
Ave (N1 1 N2 1 N3) vs. mean
Ave (N1 1 N2 1 N3 1 N4) vs. mean
BLX
TIM
IAM
CUL
ASA
FRQ
POW
DZX
DTM
DCL
DFQ
.952
.972
.981
.994
.946
.972
.985
.995
.976
.986
.992
.997
.913
.955
.982
.996
.957
.982
.993
.998
.938
.968
.991
.998
.975
.989
.992
.997
.927
.978
.986
.998
.928
.979
.986
.998
.925
.975
.987
.998
.880
.964
.982
.997
.938
.971
.991
.998
.414
.838
.983
.979
.985
.992
.997
1.000
.888
.962
.984
.997
.880
.960
.983
.997
.953
.977
.990
.998
.414
.838
.938
.979
.945
.978
.987
.997
.945
.978
.987
.997
.855
.952
.981
.996
.835
.942
.977
.996
.828
.918
.980
.996
.824
.925
.973
.986
.801
.877
.979
.993
.861
.939
.976
.993
.851
.937
.976
.993
.870
.955
.984
.995
.824
.925
.973
.986
All values are for the average artifact-free 30-sec epoch of non–rapid eye movement stages 2– 4 sleep. N 5 16; r 5 .482, p , .05; r 5 .606, p , .01. PAA zero cross
measures: BLX, no. of half-waves; TIM, time (sec) occupied by waves in frequency band; IAM, integrated amplitude (mV z sec) in frequency band; CUL, peak-trough
amplitude (mV) of waves in frequency band; ASA, average sample amplitude (mV) of waves in frequency band; FRQ, mean frequency (Hz). POW, FFT-measured power
(mV2 z sec). PAA zero derivative measures: DZX, half-waves; DTM, time; DCL, curve length; DFQ, mean frequency. Units are the same as those of zero cross measures.
1018
X. Tan et al
BIOL PSYCHIATRY
2000;48:1010 –1019
amplitudes, are highly reliable in the three frequency
bands.
In a discussion of PAA versus PSA, Reynolds and
Brunner (1995) stated: “If wave amplitude and incidence
of EEG frequencies are expected to be differently affected
by mental illness or external perturbation (challenges)
PAA should be used.” But this begs the question of how
one could know in advance whether to expect differential
effects. Thus far, PAA has shown differential effects on
human sleep EEG of age (Feinberg et al 1981, 1990),
hypnotics (Feinberg et al 1979), naps (Feinberg et al 1985,
1992), and sleep deprivation (Feinberg et al 1987). In rats,
differential effects on EEG amplitude and incidence are
produced by sleep deprivation (Feinberg and Campbell
1993b), ambient light (Campbell and Feinberg 1993), and
N-methyl-D-aspartate receptor blockade (Feinberg and
Campbell 1993a). These observations indicate that PAA
should be included in any study where the differential
effects on the incidence and amplitude of EEG waves that
alter spectral power might be theoretically or clinically
important. The recent demonstration by Uchida et al
(1999) that the PAA zero derivative analysis gives good
agreement with FFT power in the faster frequencies adds
confidence to its use for measuring amplitude and incidence of fast EEG. It is efficient to employ software such
as PASS PLUS, which simultaneously applies validated
PAA methods and standard FFT analysis to the same
digitized data (for an example of the value of this
combination in rat sleep, see Campbell and Feinberg
1999).
The almost perfect internight correlations for FFT
power in the sigma band (median r 5 .977) merit
comment. Assuming that this is not a chance result, one
wonders whether these correlations are so high because
there are marked and stable individual differences in
organized spindle activity. Uchida et al (1991) suggested
that spindles in normal young adults probably dominate
FFT power in the sigma band, a hypothesis verified
experimentally by Dijk et al (1993). Another factor contributing to the high reliability of spindles might be that
they are particularly well suited for PSA measurement
because their waveforms more closely approximate the
Fourier assumptions of sinusoidal shape and (within our
;5-sec epoch length) stationarity.
One question raised by our findings is whether the
stability of the NREM delta EEG across nights is consistent with its postulated role as a marker of sleep homeostasis. According to the homeostatic model as initially
formulated (Feinberg 1974), NREM delta is a correlate of
a process by which the brain reverses the “neurometabolic” effects of plastic waking processes. The “two
process” homeostatic model (Borbely 1982) makes a
similar inference, although less explicitly. In this experi-
ment we did not control subjects’ daytime activities, apart
from time awake; however, it was anecdotally apparent
that these activities could vary considerably over the 5
days of study. At times, subjects studied intensively for
examinations until just before bedtime, and on other days
they relaxed and watched television. Their physical activities and participation in sports also varied from day to
day. Nevertheless, delta was stable across nights and
internight correlations were consistently high. This finding
suggests that normal variations in daytime behavior of
college students have little effect on NREM delta (or
sigma and beta) so long as wake time is controlled. This
result is more consistent with “traitlike” than “statelike”
behavior.
This work was supported by a University of California, Davis Faculty
Research Award (IF); by Lorex Pharmaceuticals; by the Department of
Veterans Affairs; and by U.S. Public Health Service Grants No.
R01MH50741 and No. R01MH57928.
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