The nature of statistical learning theory
The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
A new technique to reduce cross terms in the Wigner distribution
Digital Signal Processing
Automatic seizure detection based on time-frequency analysis and artificial neural networks
Computational Intelligence and Neuroscience - Regular issue
Journal of Medical Systems
Non-linear analysis of EEG signals at various sleep stages
Computer Methods and Programs in Biomedicine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Assessment of the EEG complexity during activations from sleep
Computer Methods and Programs in Biomedicine
Texture classification using spectral histograms
IEEE Transactions on Image Processing
Computer Methods and Programs in Biomedicine
An ensemble system for automatic sleep stage classification using single channel EEG signal
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
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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.