Automatic sleep stages classification using optimize flexible analytic wavelet transform

Sleep stages classification avails the diagnosis and treatment of sleep-related disorders. The traditional visual inspection methods used by sleep-experts are time-consuming and error-prone. This framework proposes, an automatic sleep stages classification method based on optimize flexible analytic wavelet transform (OFAWT) for electroencephalogram (EEG) signals. In OFAWT, the parametric optimization is performed to obtain the most appropriate basis for the representation of EEG signals. The OFAWT parameters are selected by solving inequality constraints problem using the genetic algorithm. OFAWT decomposes EEG signal into band-limited basis or sub-bands (SBs). Time domain measures of SBs are used as features for the sleep stages EEG signals. The statistical significance of extracted features is assessed by multiple-comparison post hoc analysis of Kruskal–Wallis test, which ensures that reported features are statistically significant for the discrimination of sleep stages. The SB-wise features set is tested through the variants of decision tree, discriminant analysis, k-nearest neighbor, and ensemble classifiers for sleep stages classification. The ensemble classification model bagged-tree yields better classification accuracies for the classification of six to two sleep stages 96.03%, 96.39%, 96.48%, 97.56%, and 99.36%, respectively as compared to other existing methods.