Classification of sleep stages using multi-wavelet time frequency entropy and LDA

Abstract

The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Keywords: Sleep stage scoring, Multi-wavelets, Time-frequency entropy, Linear discriminant analysis