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 Wigne...

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Bibliographic Details
Published in:Computer methods and programs in biomedicine 2013-12, Vol.112 (3), p.320-328
Main Authors: Bajaj, Varun, Pachori, Ram Bilas
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Automatic sleep stage classification
Automation
Biological and medical sciences
Computer science
control theory
systems
Data processing. List processing. Character string processing
Electrodiagnosis. Electric activity recording
Electroencephalogram (EEG) signal
Electroencephalography
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
Humans
Image processing
Internal Medicine
Investigative techniques, diagnostic techniques (general aspects)
Medical sciences
Memory organisation. Data processing
Multiclass least squares support vector machines
Nervous system
Other
Pattern recognition. Digital image processing. Computational geometry
Sleep
Sleep. Vigilance
Smoothed pseudo Wigner–Ville distribution
Software
Time-frequency analysis
Vertebrates: nervous system and sense organs
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Summary: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.
ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2013.07.006