Automatic sleep staging using a single-channel EEG modeling by Kalman Filter and HMM

This paper proposes an automatic classification system for sleep stage of persons. The sleep condition of a person is monitored by one channel electroencephalogram (EEG). Because of the non stationary nature of the signal, for the feature extraction task, it is used the coefficients of a Kalman Filter modeling. The classification task is realized by a K-Means Segmental HMM (Hidden Markov Model). To evaluate the performance of the system, it is used the MIT-BIH Polysomnographic EEG database. At the end, the results are presented and discussed.