Maximum likelihood based classification for the microstructure of human sleep

Abstract

In this paper a classifier for the microstructure paradigm of human sleep, the Cyclic Alternating Pattern Sequence (CAPS), is presented. Sleep electroencephalogram (EEG) is the signal used for the scoring. An EEG model makes the feature extraction (preprocessing). The CAPS phases are then detected using maximum likelihood (ML) estimation. A final processing block checks CAPS context rules. This system was tested with good results on the record of 8 hours sleep of a normal adult subject.