Automatic classification of sleep stages based on the time-frequency image of EEG signals

被引：173

作者：

Bajaj, Varun ^{[1
]}

Pachori, Ram Bilas ^{[1
]}

机构：

[1] Indian Inst Technol Indore, Discipline Elect Engn, Indore 452017, Madhya Pradesh, India

来源：

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE | 2013年 / 112卷 / 03期

关键词：

Electroencephalogram (EEG) signal; Automatic sleep stage classification; Multiclass least squares support vector machines; Time-frequency analysis; Smoothed pseudo Wigner-Ville distribution; Image processing; SUPPORT VECTOR MACHINE; SYSTEM; SELECTION; SEIZURE; CHANNEL;

D O I：

10.1016/j.cmpb.2013.07.006

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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. (C) 2013 Elsevier Ireland Ltd. All rights reserved.

引用

页码：320 / 328

页数：9

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