CAUSAL PROBABILISTIC NETWORK AND POWER SPECTRAL ESTIMATION USED IN SLEEP STAGE CLASSIFICATION

A new method for sleep-stage classification using a causal probabilist ic network as automatic classifier has been implemented and validated. The system uses features from the primary sleep signals from the brai n (EEG) and the eyes (AOG) as input. From the EEG, features are derive d containing spectral information which is used to classify power in t he classical spectral bands, sleep spindles and K-complexes. From AOG, information on rapid eye movements is derived. Features are extracted every 2 seconds. The CPN-based sleep classifier was implemented using the HUGIN system, an application tool to handle causal probabilistic networks. The results obtained using different training approaches sho w agreements ranging from 68.7 to 70.7% between the system and the two experts when a pooled agreement is computed over the six subjects. As a comparison, the interrater agreement between the two experts was fo und to be 71.4%, measured also over the six subjects.