Automatic detection of cyclic alternating pattern (CAP) sequences in sleep: preliminary results

Objectives: The analysis of cyclic alternating pattern (CAP) provides impor tant microstructural information on arousal instability and on EEG synchron y modulation in the sleep process. This work presents a methodology for aut omatic classification of the micro-organization of human sleep EEG, using t he CAP paradigm. Methods: The classification system is composed of 3 parts: feature extracti on, detection and classification. The feature extraction part is an EEG gen eration model-based maximum likelihood estimator. The detector part for the CAP phases A and B is done by a variable length template matched filter, w hile the classification criteria part is implemented on a state machine rul ed-based decision system. Results and conclusions: The preliminary results of the automatic classifie r on a group of 4 middle-aged adults are presented. The high agreement betw een the detector and visual scoring is very promising in the achievement of a fully automated scoring system, although a more exhaustive evaluation pr ogram is needed. (C) 1999 Elsevier Science Ireland Ltd. All rights reserved .