title: Automatic classification of sleep stages based on the time-frequency image of EEG signals.
abstract: In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals.
authors.affiliation: Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 452017, India. Electronic address: varun@iiti.ac.in.
authors.forename: Varun
authors.initials: V
authors.lastname: Bajaj
authors.forename: Ram Bilas
authors.initials: RB
authors.lastname: Pachori
mesh_terms: D001331:Automation; D004569:Electroencephalography; D006801:Humans; D012890:Sleep
keywords: Automatic sleep stage classification; Electroencephalogram (EEG) signal; Image processing; Multiclass least squares support vector machines; Smoothed pseudo Wigner–Ville distribution; Time-frequency analysis