Comput Methods Programs Biomed - Automatic classification of sleep stages based on the time-frequency image of EEG signals.

Tópicos

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Resumo

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.

Resumo Limpo

paper new method automat sleep stage classif base timefrequ imag tfi electroencephalogram eeg signal propos automat classif sleep stage import part diagnosi treatment sleep disord smooth pseudo wignervill distribut spwvd base timefrequ represent tfr eeg signal use obtain timefrequ imag tfi segment tfi perform base frequencyband rhythm eeg signal featur deriv histogram segment tfi use input featur set multiclass least squar support vector machin mclssvm togeth radial basi function rbf mexican hat wavelet morlet wavelet kernel function automat classif sleep stage eeg signal experiment result present show effect propos method classif sleep stage eeg signal

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