SLEEP STAGE CLASSIFICATION USING SPARSE RATIONAL DECOMPOSITION OF SINGLE CHANNEL EEG RECORDS

A sparse representation of 1D signals is proposed based On time-frequency analysis using Generalized Rational Discrete Short Time Fourier Transform (RDSTFT). First, the signal is decomposed into a set of frequency sub-bands using poles and coefficients of the RDSTFT spectra. Then, the sparsity is obtained by applying the Basis Pursuit (BP) algorithm on these frequency sub-bands. Finally, the total energy of each sub band was used to extract features for offline patient-specific sleep stage classification of single channel EEG records. In classification of over 670 hours sleep Electroencephalography of 39 subjects, the overall accuracy of 92.50% on the test set is achieved using random forests (RF) classifier trained on 25% of each sleep record. A comparison with the results of other state-of-art methods demonstrates the effectiveness of the proposed sparse decomposition method in EEG signal analysis.