Feature Extraction and Selection for Automatic Sleep Staging using EEG

Sleep disorders affect a great percentage of the population. The diagnostic of these disorders is usually made by a polysomnography, requiring patient’s hospitalization. Low cost ambulatory diagnostic devices can in certain cases be used, especially when there is no need of a full or rigorous sleep staging. In this paper, several methods to extract features from 6 EEG channels are described in order to evaluate their performance. The features are selected using the R-square Pearson correlation coefficient (Guyon and Elisseeff, 2003), providing this way a Bayesian classifier with the most discriminative features. The results demonstrate the effectiveness of the methods to discriminate several sleep stages, and ranks the several feature extraction methods. The best discrimination was achieved for relative spectral power, slow wave index, harmonic parameters and Hjorth parameters.