The complexity of the electroencephalogram (EEG) during human sleep can be estimated by calculating the correlation dimension. Due to the large number of calculations required by this approach, only selected short (4-164 s) segments of the sleep EEG have been analysed previously. By using a new type of personal supercomputer, we were able to calculate the correlation dimension of overlapping 1 min EEG segments for the entire sleep episode (480 min) of 11 subjects and thereby delineate the time course of the changes. The correlation dimension was high in episodes of rapid eye movement (REM) sleep, declined progressively within each non-REM sleep episode, and reached a low level at times when EEG slow waves (0.75-4.5 Hz) were dominant. However, whereas slow-wave activity showed its typical progressive decline from non-REM/REM sleep cycle 1 to 4, no such trend was present for the correlation dimension. By providing an estimate of the complexity of a signal and being independent of amplitude and frequency measures, the correlation dimension represents a novel approach to exploring the dynamics of sleep and the processes underlying its regulation.
