Automatic detection of cyclic alternating pattern (CAP) sequences in sleep: preliminary results.
OBJECTIVE
The analysis of cyclic alternating pattern (CAP) provides important microstructural information on arousal instability and on EEG synchrony modulation in the sleep process. This work presents a methodology for automatic classification of the micro-organization of human sleep EEG, using the CAP paradigm.


METHODS
The classification system is composed of 3 parts: feature extraction, detection and classification. The feature extraction part is an EEG generation model-based maximum likelihood estimator. The detector part for the CAP phases A and B is done by a variable length template matched filter, while the classification criteria part is implemented on a state machine ruled-based decision system.


CONCLUSIONS
The preliminary results of the automatic classifier on a group of 4 middle-aged adults are presented. The high agreement between the detector and visual scoring is very promising in the achievement of a fully automated scoring system, although a more exhaustive evaluation program is needed.
Rosa
A C
AC
Systems and Robotics Institute - IST, Lisbon, Portugal.
Parrino
L
L
Terzano
M G
MG
D000328:Adult; D001921:Brain / Q000502:physiology; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008297:Male; D010507:Periodicity; D017286:Polysomnography; D012894:Sleep Stages / Q000502:physiology*