Automatic Sleep Stages Detection Based on EEG Signals Using Combination of Classifiers

Kianzad, R.; Montazery Kordy, H.

doi:10.22061/jecei.2013.30

Document Type : Original Research Paper

Authors

Babol Noshirvani University of Technology, Babol, Iran

https://doi.org/10.22061/jecei.2013.30

Abstract

Sleep stages classification is one of the most important methods for diagnosis in psychiatry and neurology. In this paper, a combination of three kinds of classifiers are proposed which classify the EEG signal into five sleep stages including Awake, N-REM (non-rapid eye movement) stage 1, N-REM stage 2, N-REM stage 3 and 4 (also called Slow Wave Sleep), and REM. Twenty-five all night recordings from Physionet database are used in this study. EEG signals were decomposed into the frequency sub-bands using wavelet packet tree (WPT) and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Then, these statistical features are used as the input to three different classifiers: (1) Logistic Linear classifier, (2) Gaussian classifier and (3) Radial Basis Function classifier. As the results show, each classifier has its own characteristics. It detects particular stages with high accuracy but, on the other hand, it has not enough success to detect the others. To overcome this problem, we tried the majority vote combination method to combine the outputs of these base classifiers to have a rather good success in detecting all sleep stages. The highest classification accuracy is obtained for Slow Wave Sleep as 81.68% in addition to the lowest classification accuracy of 43.68% for N-REM stage 1. The overall accuracy is 70%.

Keywords

20.1001.1.23223952.2013.1.2.5.7

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Journal of Electrical and Computer Engineering Innovations (JECEI)

Automatic Sleep Stages Detection Based on EEG Signals Using Combination of Classifiers

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Volume 1, Issue 2 - Serial Number 2
July 2013
Pages 99-105

Automatic Sleep Stages Detection Based on EEG Signals Using Combination of Classifiers

References

References

Send comment about this article

Volume 1, Issue 2 - Serial Number 2July 2013Pages 99-105

Volume 1, Issue 2 - Serial Number 2
July 2013
Pages 99-105