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.
journal_name
Comput Methods Programs Biomedjournal_title
Computer methods and programs in biomedicineauthors
Bajaj V,Pachori RBdoi
10.1016/j.cmpb.2013.07.006subject
Has Abstractpub_date
2013-12-01 00:00:00pages
320-8issue
3eissn
0169-2607issn
1872-7565pii
S0169-2607(13)00226-5journal_volume
112pub_type
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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