Macrostructural EEG characterization based on nonparametric change point segmentation: application to sleep analysis.

In the present investigation a new methodology for macrostructural EEG characterization based on automatic segmentation has been applied to sleep analysis. A nonparametric statistical approach for EEG segmentation was chosen, because it minimizes the need for a priori information about a signal. The method provides the detection of change-points i.e. boundaries between quasi-stationary EEG segments based on the EEG characteristics within four fundamental frequency bands (delta, theta, alpha and beta). Polysomnographic data of 18 healthy subjects were analyzed. Our findings show that nonparametric change-point segmentation in combination with cluster analysis enables us to obtain a clear picture of the hierarchical macrostructural organization of sleep, which is impossible to deduce from the unsegmented EEG data. Analysis of correlations between classically defined sleep stages and piecewise stationary power step functions reveals that three basic patterns can be distinguished: SWS (stage III/stage IV), stage II and stage I/REM. In accordance with correlation analyses, cluster detection shows that the cyclic sleep patterns during the course of the night become clearly observable by implementation of only three classes. Since the described methodology is based on a minimum of a priori assumptions, it may be useful for the development of a new sleep classification standard, which goes beyond the established Rechtschaffen and Kales scheme.