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Automatic Sleep Stages Detection Based on EEG Signals Using Combination of Classifiers | ||
| Journal of Electrical and Computer Engineering Innovations (JECEI) | ||
| مقاله 5، دوره 1، شماره 2، مهر 2013، صفحه 99-105 اصل مقاله (261.06 K) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22061/jecei.2013.30 | ||
| نویسندگان | ||
| R. Kianzad* ؛ H. Montazery Kordy | ||
| Babol Noshirvani University of Technology, Babol, Iran | ||
| تاریخ دریافت: 25 شهریور 1391، تاریخ بازنگری: 24 مرداد 1392، تاریخ پذیرش: 31 مرداد 1392 | ||
| چکیده | ||
| 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%. | ||
| کلیدواژه‌ها | ||
| Sleep stages classification؛ EEG signals؛ Wavelet packets؛ Classifier combination؛ Majority voting | ||
| مراجع | ||
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[1] M. E. Tagluk, N. Sezgin, and M. Akin, “Estimation of sleep stages by an artificial neural network employing EEG, EMG and EOG,” J Med Syst, vol. 34, pp. 717–725, 2010.
[2] A. Rechtschaffen and A. Kales, “A manual of standardized terminology, techniques and scoring systems for sleep Stages of human subjects,” US Government Printing Office, Washington, 1969.
[3] M. Reite, D. Buysse, C. Reynolds, and W. Mendelson, “The use of polysomnography in the evaluation of insomnia,” Sleep, vol. 18, pp. 58–70, 1995.
[4] “American Academy of Sleep Medicine Task Force. Sleep related breathing disorders in adults: Recommendations for syndrome definition and measurement techniques in clinical research,” Sleep, vol. 22(5), pp. 667–689, 1999.
[5] G. Becq, S. Charbonnier, F. Chapotot, A. Buguet, L. Bourdon, and P. Baconnier., “Comparison between five classifiers for automatic scoring of human sleep recordings,” Studies in Computational Intelligence (SCI), vol. 4, pp. 113–127, 2005.
[6] E. Oropesa, H. L. Cycon, and M. Jobert, “Sleep stage classification using wavelet transform and neural network,” International Computer Science Institute (ICSI), 1999.
[7] M. Kiymik, M. Akin, and A. Subasi, “Automatic recognition of alertness level by using wavelet transform and artificial neural network,” J. Neuroscience Methods, vol. 139, pp. 231–240, 2004.
[8] http://www.physionet.org/pn3/ucddb/
[9] A. Subasi, “Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients,” Expert Systems with Applications, vol. 28, pp. 701–711, 2005.
[10] C. Burros, R. Goliath, and H. Guo, “Introduction to wavelets and wavelet transforms,” Prentice Hall Pub, 1998.
[11] S. Theodoridis and K. Koutroumbas, Pattern Recognition, 4rd ed., Elsevier, 2009, pp. 34–36.
[12] T. Poggio and F. Girosi, “Networks for approximation and learning,” Proc. IEEE, vol. 78, no. 9, pp. 1481–1497, 1990. [13] M. N. Murty and V. S. Devi, Pattern Recognition, vol. 0, Springer: London, 2011, pp.188–206.
[14] C. A. Shipp and L. I. Kuncheva, “Relationships between combination methods and measures of diversity in combining classifiers,” Information Fusion, vol. 3, pp. 135–148, 2002.
[15] L. Lam and C. Y. Suen, “Application of majority voting to pattern recognition: an analysis of its behavior and performance,” IEEE Transaction on systems, man, and cybernetics-Part A: systems and humans, vol. 27, no. 5, September 1997.
[16] P. Robert, W. Duin, and D. Tax, PRTools: A Matlab toolbox for pattern recognition, 2012, software available at http://prtools.org/software/. | ||
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