NDLI: Automatic detection and classification of sleep stages by multichannel EEG signal modeling

Content Provider	IEEE Xplore Digital Library
Author	Zhovna, Inna Shallom, Ilan D.
Copyright Year	2008
Description	Author affiliation: Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel (Zhovna, Inna; Shallom, Ilan D.)
Abstract	In this paper a novel method for automatic detection and classification of sleep stages using a multichannel electroencephalography (EEG) is presented. Understanding the sleep mechanism is vital for diagnosis and treatment of sleep disorders. The EEG is one of the most important tools of studying and diagnosing sleep disorders. EEG signals waveforms activity interpretation is performed by visual analysis (a very difficult procedure). This research aim is to ease the difficulties involved in the existing manual process of EEG interpretation by proposing an automatic sleep stage detection and classification system. The suggested method based on Multichannel Auto Regressive (MAR) model. The multichannel analysis approach incorporates the cross correlation information existing between different EEG signals. In the training phase, we used the vector quantization (VQ) algorithm, Linde-Buzo-Gray (LBG) and sleep stage definition, by estimation of probability mass functions (pmf) per every sleep stage using Generalized Log Likelihood Ratio (GLLR) distortion. The classification phase was performed using Kullback-Leibler (KL) divergence. The results of this research are promising with classification accuracy rate of 93.2%. The results encourage continuation of this research in the sleep field and in other biomedical signals applications.
Starting Page	2665
Ending Page	2668
File Size	224823
Page Count	4
File Format	PDF
ISBN	9781424418145
ISSN	1557170X
DOI	10.1109/IEMBS.2008.4649750
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-20
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	VQ EEG sleep signal multichannel AR GLLR LBG
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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