Sleep stages clustering using time and spectral features of EEG signals an unsupervised approach

Abstract

Sleep stage classification is a highly addressed issue in polysomnography; It is considered a tedious and time-consuming task if done manually by the specialist; therefore, from the engineering point of view, several methods have been proposed to perform an automatic sleep stage classification. In this paper an unsupervised approach to automatic sleep stage clustering of EEG signals is proposed which uses spectral features related to signal power, coherences, asymmetries, and Wavelet coefficients; the set of features is classified using a clustering algorithm that optimizes a cost function of minimum sum of squares. Accuracy and kappa coefficients are comparable to those of the current literature as well as individual stage classification results. Methods and results are discussed in the light of the current literature, as well as the utility of the groups of features to differentiate the states of sleep. Finally, clustering techniques are recommended for implementation in support systems for sleep stage scoring.