high internight reliability of computer-measured nrem delta, sigma, and beta: biological...

High Internight Reliability of Computer-MeasuredNREM Delta, Sigma, and Beta: Biological Implications

Xin Tan, Ian Glenn Campbell, Laura Palagini, and Irwin Feinberg

Background: Computer analysis of the sleep electroen-cephalogram (EEG) waveforms is widely employed, butthere have been no systematic studies of its reliability.

Methods: The most commonly used computer methods arepower spectral analysis with the fast-Fourier transform(FFT) and period amplitude analysis (PAA) with zerocross or zero first derivative half-wave measurement. Weapplied all three computer methods to the digitized EEG of16 normal subjects who underwent 5 consecutive nights ofbaseline (placebo) recording. We evaluated the internightreliability 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 gaveexcellent internight reliability for delta and sigma. Even asingle night of recording correlated highly (r . .9) withthe 5-night mean. Beta reliability was lower but still highlysignificant for both the PAA and the FFT measures.

Conclusions: Computer-analyzed sleep EEG data arehighly reliable. Period amplitude methods demonstratethat wave incidence and period as well as amplitude arereliable, indicating that the reliability of composite mea-sures (FFT power, PAA integrated amplitude) is not solelybased on individual differences in EEG amplitude. Thehigh internight stability of NREM delta indicates that itpossesses traitlike characteristics and is relatively inde-pendent of day-to-day variations in state.Biol Psychia-try 2000;48:1010–1019 ©2000 Society of BiologicalPsychiatry

Key Words: Sleep EEG, computer, reliability,homeostasis

Introduction

The value of computer measurement of sleep electro-encephalogram (EEG) waveforms is now generally

accepted. The two most widely employed methods are

period amplitude analysis (PAA) with zero cross and zeroderivative algorithms, and power spectral analysis (PSA)with the fast-Fourier transform (FFT). In spite of increas-ing use of these methods, there are few published data ontheir internight reliability or on the absolute magnitudes ofthe differences that occur across baseline nights. Such datacould help formulate experimental designs, such as includ-ing estimates of experimental power and decisions on thenumber of baseline nights to record. Knowledge of theabsolute magnitudes of internight variation under baselineconditions could also be useful for evaluating experimen-tal effects reported in the literature.

We are currently carrying out a large-scale investigationof the reliability of computer-measured sleep EEG underbaseline conditions in young adults. Our analyses includea wide frequency range (0–100 Hz) in both non–rapid eyemovement (NREM) and REM sleep. Here we presentinitial results for three NREM frequency bands of partic-ular interest for the biology of sleep: delta (0.3–3 Hz),sigma (12–15 Hz), and beta (15–23 Hz). The delta band isof interest because of its close relation to maturation andaging (Feinberg et al 1967, 1981, 1990; Williams et al1974) over the human life span and its central role inhomeostatic models (Borbely 1982; Feinberg 1974).Sigma and beta are also of considerable interest. Orga-nized spindles in 12–15 Hz are a distinguishing hallmarkof the NREM EEG. Next to delta, organized spindles arethe waveforms that show greatest decline between youngadulthood and normal old age (Guazzelli et al 1986). It isof further biological interest that sigma and beta powerexhibit systematic dynamic relations to delta, relations thathave spurred considerable research interest (Aeschbach etal 1997; Borbely 1998) since their initial descriptions byUchida et al (1991).

Methods and Materials

SubjectsThe data for these analyses were obtained in a study thatcompared the sleep EEG effects of threeg-aminobutyric acid–ergic hypnotics to placebo. The drugs were zolpidem (10 mg),triazolam (0.25 mg), and temazepam (15 mg), given a half hourbefore sleep in capsules identical to placebo. Preliminary reports

From the Department of Psychiatry, University of California, Davis (XT, IGC, IF),Psychiatry Clinic, University of Pisa, Pisa, Italy (LP), and Veterans Adminis-tration 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 0006-3223/00/$20.00PII S0006-3223(00)00873-8

of the drug effects on NREM delta, sigma, and beta frequencieshave been presented (Feinberg et al 1995a, 1995b). The studywas comprised of four treatment arms with sleep laboratoryrecording on 5 consecutive nights. In each arm, subjects receivedone of the active drugs or placebo for the first 3 nights and thenplacebo for the final 2 nights. Therefore, in one treatment arm,subjects received placebo for 5 consecutive nights. These pla-cebo data were used for the reliability analyses. Subjects werestudents at University of California, Davis who gave informedconsent and were paid for their participation. There were 10 maleand six female subjects between the ages of 19 and 26 years(mean5 20.1, SD5 2.5). All were nonsmokers, within 25% ofthe desirable weight for their height (according to the Metropol-itan Life Insurance Table), and in excellent health according tomedical and psychiatric evaluations and a laboratory screen. Nosubject used alcohol or other drugs of abuse during the study, andurine drug screens were routinely performed. Time in bed wasmeticulously controlled, with subjects in bed from 11:30PM to7:00AM on each recording night and for the 3 nights at home thatpreceded the laboratory recordings. Daytime naps were prohib-ited. The protocol required 2 nights of polygraphic screening torule out apnea and myoclonus and to establish that subjects hadnormal sleep latencies (SLs), total sleep time (TST), and stages3–4 sleep. Criteria for acceptance were SL mean for 2 nights,20 min, TST mean for 2 nights of at least 400 min out of the450-min recording period, and combined stages 3–4$ 15%TST.

Recording and CalibrationThe C3-A2 EEG was recorded continuously with a Grass(Quincy, MA) Model 78 polygraph. A half-amplitude low-frequency filter was set at 0.3 Hz, and a high frequency filter at0.1 kHz. The preamplifier output was digitized at 200 Hz. Thedigitized values were saved to optical disk and analyzed withPASS PLUS (Delta Software, St. Louis). Of particular impor-tance for quantitative EEG studies is careful calibration. PASSPLUS analyzed a calibrated 3.5-Hz, 200-mV peak-to-peak sinewave before each night’s recording and scaled the PAA and PSAmeasurements on each channel to this standard.

Analyses of Sleep EEGVisual scoring was performed on the ink-written record asrequired by the research protocol. The scoring was done on30-sec epochs without knowledge of drug condition (“blind”) bytwo raters, with discrepancies resolved by a third rater. Re-chtschaffen and Kales (1968) criteria for sleep stages wereapplied. Movement and other artifacts were also scored. Corre-spondence between the visual scoring of the ink-written recordand the computer measures was accomplished with a computer-generated time code written on the polygraph record every 10 secby the digital-to-analog converter (with PASS PLUS on-screenscoring, this correspondence is automatic). The computer datareported below are based on all visually categorized, artifact-freeepochs of NREM scored as stages 2–4 on each baseline(placebo) night. There were, on average, 598 NREM (stages2–4) epochs per night.

Computer AnalysesPAA WITH PASS PLUS. Two methods of detection and

measurement are simultaneously applied by PASS PLUS periodanalysis: half-wave detection by successive crossings of zerovoltage and by successive zero first derivative points. Zero cross(also called baseline crossing) analysis is more effective for slowfrequencies. Zero derivative analysis is more suitable for fastEEG waves, which are often superimposed on slower activityand do not cross zero voltage. The algorithms for both PAAmethods have been published, along with initial data on theirreliability and the reproducibility of the absolute values obtainedin similar groups (Feinberg et al 1978, 1980). Linear interpola-tion has been incorporated in PASS PLUS PAA algorithms sincetheir inception. Such interpolation greatly improves resolution ofwave periods (frequencies) without the processing and storagecosts of high sample rates (“oversampling,” J.D. March, unpub-lished manuscript). Both the zero cross and the zero derivativePAA yield separate estimates for wave number, period, andamplitude. 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 crossalgorithms, we report here the internight reliability data for zerocross measures for sigma and beta as well as for delta. (Normallyour laboratory uses zero cross measures for delta and zeroderivative measures for all higher frequencies, including sigmaand beta.) The specific PAA measures and their definitions areshown in Table 1 (for details, see Feinberg et al 1978).

PSA WITH PASS PLUS. Fast-Fourier transform was per-formed on 30-sec epochs of 5.120-sec Welch tapered windowswith 2.620-sec overlap. This yielded 12 windows per 30-secepoch. The bands used in the analyses here were 0.3–3 Hz fordelta, 12–15 Hz for sigma, and 15–23 Hz for beta. (The actualfrequency bands differ slightly from these nominal values: deltais 0.29–3.03; sigma is 12.01–14.94, and beta is 14.94–22.95).

Fast-Fourier transform analysis yielded the classical measureof power inmV2 z sec for each frequency band.

Table 1. Period Amplitude Analysis Measures and TheirDefinitions

Zero cross measures for each frequency bandNumber 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, inmV)Mean frequency (FRQ; [BLX/2]/TIM, in Hz)

Zero first derivative measures for each frequency bandDerivative half-wave count (DZX; the number of zero derivative half-

waves)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)

Internight Reliability of Sleep EEG 1011BIOL PSYCHIATRY2000;48:1010–1019

Results

Table 2 lists the group means and SEs for each night foreach computer measure. These means were remarkablystable, being virtually identical on each of the 5 nights forboth PAA and PSA measures; however, stability of groupmeans across nights can mask large individual variationsbecause subjects whose values are higher on one nightcould be offset by subjects whose values vary in theopposite direction. Table 3, which presents the averagewithin-subject difference across successive nights, showsthat this did not occur. Night–to-night individual differ-ences for each measure were quite small. There was notrend for mean internight differences per subject to in-crease or decrease across the 5 consecutive baselinenights.

Figures 1 and 2 plot the values across 5 baseline nightsfor PAA integrated amplitude and FFT power in the deltaand sigma bands for the subjects with the highest andlowest 5-night means. These subjects were chosen becausethey might be expected to show the greatest variationbecause of the tendency toward regression to the mean. Infact, the values for even these extreme subjects were quitestable across the 5 nights.

As would be expected from the remarkable stability ofthe group means and the small within-subject differencesacross nights, the reliability of these computer-measuredEEG frequencies as estimated by Pearson correlationcoefficients was quite high. These data are shown in Table4, which presents the correlation coefficients for eachmeasure across successive nights for delta, sigma, and betacomputed for the average 30-sec epoch of NREM sleep.The five zero cross measures of delta showed consistentlyhigh internight correlations. There was no tendency fortheir correlation coefficients to increase or decrease acrosssuccessive nights. Delta power with PSA showed a medianinternight correlation of .857, quite close to the .880 fordelta integrated amplitude, the similar PAA measure.

Table 4 shows the internight reliability for sigma.Although our laboratory uses zero derivative PAA mea-sures for sigma, we also present the zero cross results,since most laboratories that use PAA apply only zero crossmeasurement. With the exception of derivative meanfrequency, which had low and statistically insignificantcorrelations, both the zero cross and the zero derivativemeasures for sigma showed high internight correlations.The correlations for sigma FFT power, although notsignificantly greater than those for the corresponding PAAmeasures, were notably high, with a median of .977 and anarrow range of .965–.985.

Table 4 also shows that internight correlations for PAAmeasures of NREM beta EEG were also substantial.Somewhat surprisingly, the reliability for the beta zero

cross measures was about equal to that of the zeroderivative measures. The median correlation of .706 forbeta power with PSA was somewhat lower than its PAAequivalent (.822), although this difference was not statis-tically significant.

Figures 3–5 present night 1–night 2 scattergrams forintegrated amplitude (PAA zero cross) and FFT power fordelta, and derivative curve length (PAA zero first deriva-tive) and FFT power for sigma and beta bands. Nights 1and 2 were chosena priori because they might beexpected to have the poorest correlations because ofreadaptation effects, and thereby more strongly challengethe reliability of the computer measures. Figures 3–5demonstrate strong linear relationships and also show thatthe correlation coefficients do not depend upon outlyingpoints. We also plotted scattergrams for all other correla-tions, and none depended upon outlying points. However,a markedly aberrant point dominated the night 1–night 2correlation for derivative mean frequency, reducing itscorrelation coefficient to near zero (Table 4). With thispoint removed, the correlation increased from2.093 to.787.

In designing sleep experiments, one important decisionis the number of baseline nights to record. We assumedthat, for most experiments, the largest practical numberwould be 5. We therefore tested the number of baselinenights required to obtain high correlations with the 5-nightmean. These results are shown in Table 5. For delta andsigma FFT power and their PAA equivalents, any singlenight, including the first, provided a high correlation withthe 5-night mean that was not appreciably increased byadding additional nights. For PAA measures of amplitude,incidence, and period, 2 baseline nights appeared toimprove the correlation. Beta waveform measures ap-peared to require 2–3 nights for adequate correlations withthe 5-night mean with both PSA and PAA.

Discussion

There are few published studies to which we can compareour findings. The most extensive previous reliabilityanalyses of PAA zero cross measures were reported whenwe described the PAA algorithms (Feinberg et al 1978,1980). In the first study, correlation coefficients for deltazero cross measures across 2 baseline nights for theaverage 20-sec epoch of NREM were high, ranging froma low of .86 to a high of .91 (N 5 20). It was presumablythis high reliability that allowed us to detect significantcorrelations of delta measures with age, even within thenarrow range of 18–23 years. In the second study, the firstfour cycles of an extended night and a recovery night werecompared for zero cross measures up to 23 Hz. Despite thefact that correlations were computed across different

1012 X. Tan et alBIOL PSYCHIATRY2000;48:1010–1019

Tab

le2.

Mea

ns(S

Es)

for

Eac

hof

5C

onse

cutiv

eB

asel

ine

(Pla

cebo

)N

ight

sfo

rP

erio

dA

mpl

itude

Ana

lysi

s(P

AA

)Z

ero

Cro

ssan

dZ

ero

Der

ivat

ive

Mea

sure

san

dF

ast-

Fou

rier

Tra

nsfo

rm(F

FT

)P

ower

BLX

TIM

IAM

CU

LA

SA

FR

QP

OW

DZ

XD

TM

DC

LD

FQ

Del

ta(0

.3–3

Hz)

N1

49.5

(1.0

)14

.8(0

.40)

343

(18.

1)6.

96(0

.27)

23.1

(0.8

5)1.

68(0

.02)

156

(13.

9)N

249

.8(1

.1)

14.9

(0.4

0)34

9(1

7.5)

7.25

(0.2

8)23

.3(0

.83)

1.67

(0.0

2)16

0(1

3.4)

N3

50.5

(0.9

)15

.2(0

.35)

352

(19.

2)7.

23(0

.34)

23.1

(0.9

5)1.

67(0

.02)

162

(15.

7)N

450

.5(1

.3)

15.1

(0.4

6)35

5(1

5.9)

7.33

(0.2

6)23

.6(0

.74)

1.68

(0.0

2)16

5(1

2.3)

N5

50.2

(1.1

)15

.0(0

.39)

349

(15.

5)7.

23(0

.28)

23.2

(0.7

5)1.

68(0

.02)

159

(11.

8)

Sig

ma

(12–

15H

z)N

127

.9(1

.53)

1.04

(0.0

6)6.

63(0

.55)

0.65

(0.0

5)6.

27(0

.26)

13.4

3(0

.01)

3.11

(0.4

0)82

.7(4

.25)

3.12

(0.1

6)1.

74(0

.80)

13.2

4(0

.03)

N2

27.6

(1.5

6)1.

03(0

.06)

6.69

(0.5

8)0.

67(0

.06)

6.37

(0.2

9)13

.44

(0.0

1)3.

13(0

.42)

80.6

(4.4

7)3.

04(0

.17)

1.72

(0.9

4)13

.26

(0.0

2)N

326

.9(1

.37)

1.00

(0.0

5)6.

39(0

.51)

0.64

(0.0

5)6.

27(0

.26)

13.4

3(0

.01)

3.04

(0.4

0)80

.4(3

.77)

3.04

(0.1

4)1.

69(0

.91)

13.2

3(0

.04)

N4

27.3

(1.5

1)1.

01(0

.06)

6.55

(0.5

9)0.

66(0

.07)

6.30

(0.2

9)13

.45

(0.0

1)3.

11(0

.38)

80.2

(4.0

7)3.

01(0

.15)

1.72

(0.8

9)13

.30

(0.0

2)N

527

.3(1

.47)

1.02

(0.0

6)6.

47(0

.53)

0.64

(0.0

5)6.

25(0

.27)

13.4

4(0

.01)

3.12

(0.3

7)81

.4(4

.23)

3.07

(0.1

6)1.

72(0

.96)

13.2

6(0

.03)

Bet

a(1

5–23

Hz)

N1

47.1

(2.6

1)1.

27(0

.07)

6.13

(0.5

1)0.

82(0

.08)

4.74

(0.1

9)18

.47

(0.0

2)1.

40(0

.09)

122

(8.7

5)3.

31(0

.23)

1.92

(0.7

2)18

.5(0

.07)

N2

46.5

(2.5

7)1.

26(0

.07)

6.17

(0.5

6)0.

83(0

.09)

4.79

(0.2

1)18

.47

(0.0

2)1.

43(0

.08)

123

(8.0

6)3.

33(0

.22)

1.97

(0.8

1)18

.4(0

.05)

N3

45.5

(2.0

5)1.

23(0

.06)

5.87

(0.4

2)0.

79(0

.06)

4.70

(0.2

0)18

.46

(0.0

3)1.

37(0

.11)

123

(5.7

8)3.

32(0

.16)

1.91

(0.9

6)18

.5(0

.07)

N4

45.7

(2.7

6)1.

24(0

.08)

6.03

(0.6

4)0.

81(0

.10)

4.73

(0.2

3)18

.46

(0.0

3)1.

43(0

.12)

125

(9.2

5)3.

41(0

.25)

2.01

(0.7

7)18

.3(0

.05)

N5

46.1

(2.2

5)1.

25(0

.06)

5.91

(0.4

4)0.

79(0

.06)

4.66

(0.2

0)18

.46

(0.0

3)1.

40(0

.11)

126

(8.6

6)3.

43(0

.24)

1.97

(0.9

6)18

.4(0

.06)

All

valu

esar

efo

rth

eav

erag

ear

tifac

t-fr

ee30

-sec

epoc

hof

non–

rapi

dey

em

ovem

ent

stag

es2–

4sl

eep.

N5

16.

PA

Aze

rocr

oss

mea

sure

s:B

LX,

no.

ofha

lf-w

aves

;T

IM,

time

(sec

)oc

cupi

edby

wav

esin

freq

uenc

yba

nd;

IAM

,int

egra

ted

ampl

itude

(mV

zse

c)in

freq

uenc

yba

nd;C

UL,

peak

-tro

ugh

ampl

itude

(mV

)of

wav

esin

freq

uenc

yba

nd;A

SA

,ave

rage

sam

ple

ampl

itude

(m

V)

ofw

aves

infr

eque

ncy

band

;FR

Q,m

ean

freq

uenc

y(H

z).P

OW

,F

FT

-mea

sure

dpo

wer

(m

V2

zse

c).

PA

Aze

rode

rivat

ive

mea

sure

s:D

ZX

,ha

lf-w

aves

;D

TM

,tim

e;D

CL,

curv

ele

ngth

;D

FQ

,m

ean

freq

uenc

y.U

nits

are

the

sam

eas

thos

eof

zero

cros

sm

easu

res.


Tab

le3.

Mea

ns(S

Es)

ofIn

divi

dual

Diff

eren

ces

acro

ssS

ucce

ssiv

eN

ight

sfo

rP

erio

dA

mpl

itude

Ana

lysi

s(P

AA

)Z

ero

Cro

ssan

dZ

ero

Der

ivat

ive

Mea

sure

san

dF

ast-

Fou

rier

Tra

nsfo

rm(F

FT

)P

ower

BLX

TIM

IAM

CU

LA

SA

FR

QP

OW

DZ

XD

TM

DC

LD

FQ

Del

ta(0

.3–3

Hz)

N1–

N2

1.15

(0.2

1)0.

49(0

.09)

24.4

(4.9

7)0.

57(0

.11)

1.23

(0.2

3)0.

03(0

.01)

20.9

(3.9

4)N

2–N

31.

48(0

.31)

0.64

(0.1

4)27

.9(4

.99)

0.77

(0.1

4)1.

58(0

.25)

0.03

(0.0

1)20

.0(4

.31)

N3–

N4

1.95

(0.4

9)0.

71(0

.19)

34.1

(5.8

5)0.

81(0

.14)

1.94

(0.3

3)0.

03(0

.01)

25.4

(6.1

5)N

4–N

51.

62(0

.49)

0.61

(0.1

6)25

.6(4

.93)

0.40

(0.0

7)1.

32(0

.20)

0.03

(0.0

1)22

.4(4

.33)

Mea

nof

5ni

ghts

50.1

(0.9

9)15

.0(0

.38)

350.

0(1

6.5)

7.20

(0.2

6)23

.3(0

.78)

1.68

(0.0

2)16

0.0

(12.

8)

Sig

ma

(12–

15H

z)N

1–N

21.

83(0

.52)

0.07

(0.0

2)0.

69(0

.18)

0.08

(0.0

2)0.

40(0

.08)

0.02

(0.0

1)0.

23(0

.04)

5.05

(1.5

3)0.

20(0

.06)

0.12

(0.0

3)0.

07(0

.03)

N2–

N3

1.72

(0.3

1)0.

06(0

.01)

0.64

(0.1

6)0.

08(0

.02)

0.43

(0.0

9)0.

02(0

.01)

0.27

(0.0

4)6.

03(1

.26)

0.23

(0.0

5)0.

19(0

.04)

0.09

(0.0

3)N

3–N

42.

00(0

.50)

0.07

(0.0

2)0.

67(0

.23)

0.08

(0.0

3)0.

36(0

.09)

0.03

(0.0

1)0.

30(0

.06)

6.32

(1.2

2)0.

24(0

.05)

0.20

(0.0

4)0.

12(0

.03)

N4

–N5

1.51

(0.3

9)0.

06(0

.01)

0.61

(0.2

0)0.

08(0

.03)

0.35

(0.0

9)0.

02(0

.01)

0.31

(0.0

6)3.

12(0

.58)

0.12

(0.0

2)0.

11(0

.02)

0.08

(0.0

2)M

ean

of5

nigh

ts27

.3(1

.42)

1.02

(0.0

5)6.

55(0

.05)

0.65

(0.5

3)6.

29(0

.26)

13.4

(0.0

1)3.

10(0

.39)

81.0

(3.9

3)3.

06(0

.15)

1.72

(0.1

3)13

.3(0

.02)

Bet

a(1

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experimental conditions, the reliability coefficients werehigh and similar to those found here for the same mea-sures. In addition, these two studies demonstrated excel-lent reproducibility of the absolute delta values. We havebeen unable to find a systematic study of the reliability ofFFT-measured sleep EEG; however, there are two studiesthat indirectly point to high reliability of NREM deltapower. Larsen et al (1995) reported high reliability across2 nights for computer-scored stage 4. Since these scoreswere based on FFT-measured delta (0.5–4 Hz), theyindicate that the computer measurements were themselvesreliable 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 succes-sive baseline nights was highly stable with both linear andexponential regressions. These observations suggest (butdo not establish) that FFT power in each NREM periodwas also stable across these nights.

An unexpected result in Table 4 was that internightcorrelation coefficients for zero cross measures of sigma

and beta were about as high as those for the zero derivativemeasures. This result was unexpected because, as notedabove, one would expect zero derivative analysis toestimate fast EEG more efficiently than zero cross meth-ods because these faster waves are frequently superim-posed on slower EEG. This result may be encouraging tothose who use only zero cross PAA. It was also surprisingthat the zero cross correlations for mean frequency in bothsigma and beta were consistently higher than the zeroderivative correlations. The basis for this difference is notimmediately obvious.

Mean frequency correlations were generally substan-tially lower than those for amplitude. This may be due tothe fact that the average nightly difference per subject inmean frequency was extremely small (Table 3). By thismeasure, mean frequency measured within subjects wasremarkably stable. We suggest that the internight correla-tions were relatively low because the extremely narrowspread of the frequency data allowed measurement error toexert a proportionately greater effect on subjects’ rank-

Figure 1. (A) Mean6 SE delta integrated amplitude (IAM) withperiod amplitude analysis for each of 5 baseline nights. Alsoshown are the delta IAM for the subject (S 4) with the highestIAM and the subject (S 12) with the lowest IAM.(B) Deltafast-Fourier transform power in the same format as for IAM.●,mean;Œ, S 4;�, S 12.

Figure 2. (A) Mean6 SE sigma derivative curve length (DCL)with period amplitude analysis for each of 5 baseline nights. Alsoshown are the sigma DCL for the subject (S 14) with the highestDCL and the subject (S 12) with the lowest DCL.●, mean;Œ, S14;�, S 12.(B) Sigma fast-Fourier transform power in the sameformat as for DCL.●, mean;Œ, S 13;�, S 1.


Figure 3. Scattergram for night 2 vs. night 1 values of delta(A)integrated amplitude (IAM) and(B) power. The high correlationsdo 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 correla-tions do not depend on outlying points.

Table 4. Product Moment Correlations across Successive Baseline nights for Period Amplitude Analysis (PAA) Zero Cross andZero Derivative Measures and Fast-Fourier Transform (FFT) Power

BLX TIM IAM CUL ASA FRQ POW DZX DTM DCL DFQ

Delta (0.3–3 Hz)N1 vs. N2 .941 .930 .903 .826 .893 .890 .884N2 vs. N3 .902 .861 .891 .712 .869 .834 .905N3 vs. N4 .844 .826 .837 .669 .788 .828 .821N4 vs. N5 .863 .878 .869 .902 .868 .889 .829Median .883 .870 .880 .769 .869 .862 .857

Sigma (12–15 Hz)N1 vs. N2 .898 .900 .904 .855 .897 .443 .985 .902 .892 .965 2.093a

N2 vs. N3 .947 .949 .933 .902 .875 .733 .982 .895 .891 .912 .485N3 vs. N4 .883 .886 .877 .847 .899 .658 .971 .868 .861 .888 .342N4 vs. N5 .932 .933 .905 .860 .905 .851 .965 .976 .975 .966 .488Median .915 .917 .905 .858 .898 .696 .977 .899 .892 .939 .414

Beta (15–23 Hz)N1 vs. N2 .941 .943 .837 .820 .711 .824 .628 .896 .887 .859 .651N2 vs. N3 .940 .941 .844 .823 .698 .919 .484 .634 .664 .731 .650N3 vs. N4 .888 .888 .846 .849 .876 .937 .784 .680 .713 .784 .458N4 vs. N5 .889 .892 .859 .865 .891 .973 .815 .910 .920 .883 .546Median .915 .917 .845 .836 .794 .928 .706 .788 .800 .822 .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 crossmeasures: 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-troughamplitude (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.

aLow correlation due to single outlying point; with this point removed,r 5 .787.


ings. This could lower the correlation coefficients, eventhough the absolute values were quite close, as shown inTable 3.

The overall internight reliability of delta and sigmameasures (apart from sigma mean frequency) is remark-ably high. Although beta also shows highly significantinternight reliability, its internight correlation coefficientsare consistently lower than those of delta and sigma. Thisdifference holds for both PAA and PSA measures. It is notimmediately obvious whether the somewhat lower corre-lations for beta are due to greater biological variability orgreater measurement error for this frequency band. Thisresult may also have been influenced by the fact that thebeta band employed was much wider (9 Hz) than those ofdelta (2.7 Hz) and sigma (4 Hz).

With respect to the practical question of the number ofnights required to establish an adequate experimentalbaseline for the different frequency bands, our data indi-cate that for delta and sigma power and their PAAequivalents a single night provides a sufficiently highcorrelation with the 5-night mean for most studies. Forother PAA measures of amplitude and incidence in deltaand sigma, 2 nights improve the correlation with the5-night mean. For both PAA and PSA measures of beta, 2baseline nights appear to be required and sufficient.

It has long been obvious that there are wide andconsistent individual differences in the amplitude of hu-man EEGs. Factors that might produce these differencesinclude variations in skull impedance, volume conduction,and brain size. These differences raise the question ofwhether the high reliabilities of delta, sigma, and betapower are wholly determined by individual differences inamplitude. Period amplitude analysis but not PSA canaddress this question. Period amplitude analysis demon-strates that wave incidence and period, as well as wave

Figure 5. Scattergram for night 2 vs. night 1 values of beta(A)derivative curve length (DCL) and(B) power. The high correla-tions do not depend on outlying points.

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 PeriodAmplitude Analysis (PAA) Zero Cross and Zero Derivative Measures and Fast-Fourier Transform (FFT) Power

BLX TIM IAM CUL ASA FRQ POW DZX DTM DCL DFQ

Delta (0.3–3 Hz)N1 vs. mean .952 .946 .976 .913 .957 .938 .975Ave (N1 1 N2) vs. mean .972 .972 .986 .955 .982 .968 .989Ave (N1 1 N2 1 N3) vs. mean .981 .985 .992 .982 .993 .991 .992Ave (N1 1 N2 1 N3 1 N4) vs. mean .994 .995 .997 .996 .998 .998 .997

Sigma (12–15 Hz)N1 vs. mean .927 .928 .925 .880 .938 .414 .985 .888 .880 .953 .414Ave (N1 1 N2) vs. mean .978 .979 .975 .964 .971 .838 .992 .962 .960 .977 .838Ave (N1 1 N2 1 N3) vs. mean .986 .986 .987 .982 .991 .983 .997 .984 .983 .990 .938Ave (N1 1 N2 1 N3 1 N4) vs. mean .998 .998 .998 .997 .998 .979 1.000 .997 .997 .998 .979

Beta (15–23 Hz)N1 vs. mean .945 .945 .855 .835 .828 .824 .801 .861 .851 .870 .824Ave (N1 1 N2) vs. mean .978 .978 .952 .942 .918 .925 .877 .939 .937 .955 .925Ave (N1 1 N2 1 N3) vs. mean .987 .987 .981 .977 .980 .973 .979 .976 .976 .984 .973Ave (N1 1 N2 1 N3 1 N4) vs. mean .997 .997 .996 .996 .996 .986 .993 .993 .993 .995 .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 crossmeasures: 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-troughamplitude (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.


amplitudes, are highly reliable in the three frequencybands.

In a discussion of PAA versus PSA, Reynolds andBrunner (1995) stated: “If wave amplitude and incidenceof EEG frequencies are expected to be differently affectedby mental illness or external perturbation (challenges)PAA should be used.” But this begs the question of howone could know in advance whether to expect differentialeffects. Thus far, PAA has shown differential effects onhuman 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 areproduced by sleep deprivation (Feinberg and Campbell1993b), ambient light (Campbell and Feinberg 1993), andN-methyl-D-aspartate receptor blockade (Feinberg andCampbell 1993a). These observations indicate that PAAshould be included inany study where the differentialeffects on the incidence and amplitude of EEG waves thatalter spectral power might be theoretically or clinicallyimportant. The recent demonstration by Uchida et al(1999) that the PAA zero derivative analysis gives goodagreement with FFT power in the faster frequencies addsconfidence to its use for measuring amplitude and inci-dence of fast EEG. It is efficient to employ software suchas PASS PLUS, which simultaneously applies validatedPAA methods and standard FFT analysis to the samedigitized data (for an example of the value of thiscombination in rat sleep, see Campbell and Feinberg1999).

The almost perfect internight correlations for FFTpower in the sigma band (medianr 5 .977) meritcomment. Assuming that this is not a chance result, onewonders whether these correlations are so high becausethere are marked and stable individual differences inorganized spindle activity. Uchida et al (1991) suggestedthat spindles in normal young adults probably dominateFFT power in the sigma band, a hypothesis verifiedexperimentally by Dijk et al (1993). Another factor con-tributing to the high reliability of spindles might be thatthey are particularly well suited for PSA measurementbecause their waveforms more closely approximate theFourier assumptions of sinusoidal shape and (within our;5-sec epoch length) stationarity.

One question raised by our findings is whether thestability of the NREM delta EEG across nights is consis-tent with its postulated role as a marker of sleep homeosta-sis. According to the homeostatic model as initiallyformulated (Feinberg 1974), NREM delta is a correlate ofa process by which the brain reverses the “neurometa-bolic” effects of plastic waking processes. The “twoprocess” homeostatic model (Borbely 1982) makes asimilar inference, although less explicitly. In this experi-

ment we did not control subjects’ daytime activities, apartfrom time awake; however, it was anecdotally apparentthat these activities could vary considerably over the 5days of study. At times, subjects studied intensively forexaminations until just before bedtime, and on other daysthey relaxed and watched television. Their physical activ-ities and participation in sports also varied from day today. Nevertheless, delta was stable across nights andinternight correlations were consistently high. This findingsuggests that normal variations in daytime behavior ofcollege students have little effect on NREM delta (orsigma and beta) so long as wake time is controlled. Thisresult is more consistent with “traitlike” than “statelike”behavior.

This work was supported by a University of California, Davis FacultyResearch Award (IF); by Lorex Pharmaceuticals; by the Department ofVeterans Affairs; and by U.S. Public Health Service Grants No.R01MH50741 and No. R01MH57928.

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