Abstract.
A new method for sleep-stage classification using a causal probabilistic network as
automatic classifier has been implemented and validated. The system uses features
from the primary sleep signals from the brain (EEG) and the eyes (AOG) as input. From
the EEG, features are derived containing spectral information which is used to classify
power in the 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
show 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 found to be 71.4%, measured also over the six
subjects.
Keywords:
Causal Probabilistic Network - Sleep - EEG-spectraIntroduction
