Please use this identifier to cite or link to this item: https://doi.org/10.1142/S0129065710002589
Title: Analysis and automatic identification of sleep stages using higher order spectra
Authors: Acharya, U.R.
Chua, E.C.-P. 
Chua, K.C.
Min, L.C.
Tamura, T.
Keywords: bispectrum
classifier
EEG
entropy
higher order spectra
Sleep
Issue Date: Dec-2010
Citation: Acharya, U.R., Chua, E.C.-P., Chua, K.C., Min, L.C., Tamura, T. (2010-12). Analysis and automatic identification of sleep stages using higher order spectra. International Journal of Neural Systems 20 (6) : 509-521. ScholarBank@NUS Repository. https://doi.org/10.1142/S0129065710002589
Abstract: Electroencephalogram (EEG) signals are widely used to study the activity of the brain, such as to determine sleep stages. These EEG signals are nonlinear and non-stationary in nature. It is difficult to perform sleep staging by visual interpretation and linear techniques. Thus, we use a nonlinear technique, higher order spectra (HOS), to extract hidden information in the sleep EEG signal. In this study, unique bispectrum and bicoherence plots for various sleep stages were proposed. These can be used as visual aid for various diagnostics application. A number of HOS based features were extracted from these plots during the various sleep stages (Wakefulness, Rapid Eye Movement (REM), Stage 1-4 Non-REM) and they were found to be statistically significant with p-value lower than 0.001 using ANOVA test. These features were fed to a Gaussian mixture model (GMM) classifier for automatic identification. Our results indicate that the proposed system is able to identify sleep stages with an accuracy of 88.7%. © 2010 World Scientific Publishing Company.
Source Title: International Journal of Neural Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/124671
ISSN: 01290657
DOI: 10.1142/S0129065710002589
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