Electroencephalography Analysis Using Neural Network and Support Vector Machine during Sleep
- 1 Graduate School of Biomedical Engineering, Yonsei University, Seoul, Korea
- 2 Corresponding Author, Department of Medical Engineering, College of Medicine, Yonsei University, Seoul, Korea
Abstract
The purpose of this paper is to analyze sleep stages accurately using fast and simple classifiers based on the frequency domain of electroencephalography(EEG) signal. To compare and evaluate system performance, the rules of Rechtschaffen and Kales(R&K rule) were used. Parameters were extracted from preprocessing process of EEG signal as feature vectors of each sleep stage analysis system through representatives of back propagation algorithm and support vector machine (SVM). As a result, SVM showed better performance as pattern recognition system for classification of sleep stages. It was found that easier analysis of sleep stage was possible using such simple system. Since accurate estimation of sleep state is possible through combination of algorithms, we could see the potential for the classifier to be used for sleep analysis system.
- K. Susmáková, “Human Sleep and Sleep EEG,” MEASURE-MENT SCIENCE REVIEW, Vol. 4, No. 2, 2004.
- P. McNamara, I. Capellini, E. Harris, C. L. Nunn, R. A. Barton and B. Preston, “The Phylogeny of Sleep Database: A New Resource for Sleep Scientists,” The Open Sleep Journal, Vol. 1, 2008, pp. 11-14. doi:10.2174/1874620900801010011
- S. K. Yoo, C. K. Lee and H. G. Jo, “Chaotic Analysis of Electroencephalograic Signal for Sleep Quality Measurement,” ISAE 2013, Kitakyushu, 6-8 March 2013.
- M. Teplan, “Fundamentals of EEG Measurement,” IEEE Measurement Science Review, Vol. 2, 2002, pp. 1-11.
- P. J. Durka, “Time-Frequency Analyses of EEG,” Institute of Experimental Physics Department of Physics Warsaw University, August, 1996.
- H. –N. F. Girosi, “Counterpropagation Networks,” IEEE International Conference on Neural Networks, 1987, pp. 19-32.
- A. Subasi and E. Erçelebi, “Classification of EEG Signals Using Neural Network and Logistic Regression,” Computer Methods and Programs in Biomedicine, Vol. 78, No. 2, 2005, pp. 87-99. doi:10.1016/j.cmpb.2004.10.009
- E. Osuna, R. Freund and F. Girosi, “Support Vector Machines: Training and Applica-tions,” C. B. C, I. Paper, No. 144, March, 1997.