Spectral Analysis of All-Night Sleep EEG in Healthy Adults
- 1 January 1983
- journal article
- research article
- Published by S. Karger AG in European Neurology
- Vol. 22 (5) , 322-339
- https://doi.org/10.1159/000115579
Abstract
Power and coherence spectra were computed from all-night sleep EEG records in 6 healthy adult subjects. Derivations were from F3, F4, P3, P4, O1, 02, T3, and T4 to the vertex (Cz). Records were conventionally scored into sleep stages. Average power per sleep stage was maximal at frequencies 0.4–6 c/s in stage 4, at 6–10 c/s in either stage 3 or stage 4, at 12–14 c/s in stage 2 and at 14–30 c/s in stage 1. The average power range from highest values in the lowest frequency band to lowest values in the highest frequency band showed marked differences between sleep stages: It was lowest (12–14 dB) in stage 1, followed by stage 2 (20–22 dB), and stage 3 (16–28 dB), and largest in stage 4 (29–32 dB). REM sleep (15–16 sB) was between stage 1 and 2. The waking state showed an average power range of 11–15 dB. Alpha power at 8–10 c/s in occipital and parietal leads was remarkably constant during sleep, i.e. independent of sleep stage. Coherence showed maximal values at 2–8 c/s in REM sleep, at 8–12 c/s in stage 4, at 12–17 c/s in either stage 3 or 4, and at 17–30 c/s again in stage REM. There was significant coherence increase at 2–8 and 17–30 c/s from NREM to REM sleep, most pronounced between parietal to vertex derivations. Overall coherence between both occipital-to-vertex, or between occipital and parietal-to-vertex derivations, was essentially higher than in the other derivations. The results, essentially, give a comprehensive phenomenology of the dynamic spectral structure of all-night sleep EEG. They suggest that the different brain states during sleep (e.g. stage 1 NREM vs. REM) which are associated with different functions (e.g. hypnagogic hallucinations vs. dreams) differ in EEG spectral parameters if coherence is considered. Likewise, they suggest that studies of automatic sleep staging based exclusively on EEG spectral parameters appear promising.This publication has 0 references indexed in Scilit: