Classical sleep stages and the spectral content of the EEG signal.

Polygraphic sleep recordings during 12 nights in 5 healthy volunteers were classified manually into waking and the various sleep stages. The smoothed power spectra of EEG signal segments defined as waking or one of the sleep stages were calculated via segmentation of the EEG signal, using the autoregressive model, and time-dependent fuzzy clustering. The spectra were derived from the prediction coefficients of the segments. The relative power in the delta frequency band was found to increase monotonically with increasing depth of sleep, together with a parallel decrease in the alpha relative power. In most cases alpha relative power had a small peak during REM sleep, and on average the relative power in the sigma frequency band during REM sleep was smaller than the beta relative power. The power spectra from subjects with no waking alpha differed from those of subjects with abundant waking alpha mostly in the relative spectral content of stages 1 and REM. The significance of these findings is discussed in relation to future standardisation of automatic analysis of sleep recordings.

[1]  J. Makhoul,et al.  Linear prediction: A tutorial review , 1975, Proceedings of the IEEE.

[2]  Jean-Michel Gaillard,et al.  Analyse automatique des enregistrements polygraphiques de sommeil , 1971 .

[3]  P Naitoh,et al.  Spectral analysis of the EEG of dominant and non-dominant alpha subjects during waking and sleeping. , 1969, Electroencephalography and clinical neurophysiology.

[4]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1987 .

[5]  I. Feinberg,et al.  Changes in frequency and amplitude of delta activity during sleep. , 1975, Electroencephalography and clinical neurophysiology.

[6]  G Dumermuth,et al.  Spectral analysis of EEG activity in different sleep stages in normal adults. , 1972, European neurology.

[7]  I Gath,et al.  Computerized method for scoring of polygraphic sleep recordings. , 1980, Computer programs in biomedicine.

[8]  T M Itil,et al.  Digital computer classifications of EEG sleep stages. , 1969, Electroencephalography and clinical neurophysiology.

[9]  R. D. Joseph,et al.  Pattern recognition of EEG-EOG as a technique for all-night sleep stage scoring. , 1972, Electroencephalography and clinical neurophysiology.

[10]  L. Johnson,et al.  Resolution and stability in the autospectra of EEG. , 1965, Electroencephalography and clinical neurophysiology.

[11]  W. Gersch.  Spectral analysis of EEG's by autoregressive decomposition of time series , 1970 .

[12]  K J Blinowska,et al.  EEG data reduction by means of autoregressive representation and discriminant analysis procedures. , 1981, Electroencephalography and clinical neurophysiology.

[13]  R T Kado,et al.  Electroencephalographic baselines in astronaut candidates estimated by computation and pattern recognition techniques. , 1967, Aerospace medicine.

[14]  I. Gath,et al.  Computerized analysis of sleep recordings applied to drug evaluation: Midazolam in normal subjects , 1981, Clinical pharmacology and therapeutics.

[15]  G. Bodenstein,et al.  Feature extraction from the electroencephalogram by adaptive segmentation , 1977, Proceedings of the IEEE.

[16]  D Lehmann,et al.  Sleep deprivation: effect on sleep stages and EEG power density in man. , 1981, Electroencephalography and clinical neurophysiology.

[17]  D Lehmann,et al.  Computer analysis of EEG wakefulness-sleep patterns during learning of novel and familiar sentences. , 1974, Electroencephalography and clinical neurophysiology.

[18]  J R Smith,et al.  EEG sleep stage scoring by an automatic hybrid system. , 1971, Electroencephalography and clinical neurophysiology.