Study on Sleep Staging Based on Support Vector Machines and Feature Selection in Single Channel Electroencephalogram_Journal of Biomedical Engineering

Authors：

LINXiujing , XIAYongming ,  QIANSongrong

Department of Communication Science and Engineering, School of Information and Technology, Fudan University, Shanghai 200433, China;

Corresponding author：

QIANSongrong, Email: sqian@fudan.edu.cn

Keywords：

sleep stage; support vector machine; F-Score; eliminate factor

DOI：

10.7507/1001-5515.20150092

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Sleep electroencephalogram (EEG) is an important index in diagnosing sleep disorders and related diseases. Manual sleep staging is time-consuming and often influenced by subjective factors. Existing automatic sleep staging methods have high complexity and a low accuracy rate. A sleep staging method based on support vector machines (SVM) and feature selection using single channel EEG single is proposed in this paper. Thirty-eight features were extracted from the single channel EEG signal. Then based on the feature selection method F-Score's definition, it was extended to multiclass with an added eliminate factor in order to find proper features, which were used as SVM classifier inputs. The eliminate factor was adopted to reduce the negative interaction of features to the result. Research on the F-Score with an added eliminate factor was further accomplished with the data from a standard open source database and the results were compared with none feature selection and standard F-Score feature selection. The results showed that the present method could effectively improve the sleep staging accuracy and reduce the computation time.

Citation： LINXiujing, XIAYongming, QIANSongrong. Study on Sleep Staging Based on Support Vector Machines and Feature Selection in Single Channel Electroencephalogram. Journal of Biomedical Engineering, 2015, 32(3): 503-507, 513. doi: 10.7507/1001-5515.20150092 Copy

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1. MAGGARD J Y. Automation of sleep staging[D]. Ontario:University of Waterloo, 2009.
2. 李颖洁, 邱意弘, 朱贻盛.脑电信号分析方法及其应用[M].北京:科学出版社, 2009:75-87.
3. BERRY R B, BROOKS R, GAMALDO C E, et al. The AASM manual for the scoring of sleep and associated events:rules, terminology and technical specifications, version 2.0[M/CD]. Illinois:American Academy of Sleep Medicine, 2013:15-30[2014-05-20]. http://www.aasmnet.org/scoringmanual/v2.0.2/html/index.html?IXDevelopmentProcess.html.
4. FLEXER A, GRUBER G, DORFFNER G. A reliable probabilistic sleep stager based on a single EEG signal[J]. Artif Intell Med, 2005, 33(3):199-207.
5. GÜNEŞ S, POLAT K, YOSUNKAYA Ş Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting[J]. Expert Syst Appl, 2010, 37(12):7922-7928.
6. TAGLUK M E, SEZGIN N, AKIN M. Estimation of sleep stages by an artificial neural network employing EEG, EMG and EOG[J]. J Med Syst, 2010, 34(4):717-725.
7. GORUR D, HALICI U, AYDIN H, et al. Sleep spindles detection using short time Fourier transform and neural networks[C]//Proceedings of the 2002 International Joint Conference on IEEE. Honolulu:2002, 2:1631-1636.
8. WESTON J, MUKHERJEE S, CHAPELLE O, et al. Feature selection for SVMs[C]//The Neural Information Processing Systems. Colorado:2000, 12:668-674.
9. JANJARASJITT S. Examination of temporal characteristic of sleep EEG subbands based on the local min-max[C]//Biomedical Engineering International Conference. Ubon Ratchathani:IEEE, 2012:1-4.
10. REZEK I A, ROBERTS S J. Stochastic complexity measures for physiological signal analysis[J]. IEEE Trans Biomed Eng, 1998, 45(9):1186-1191.
11. CHEN Yiwei, LIN C J. Combining SVMs with various feature selection strategies[M]//Guyon I, Nikravesh M, Gunn S, et al. Feature Extraction:Foundations and Applications. Berlin Heidelberg:Springer Berlin Heidelberg, 2006, 207:315-324.
12. BURGES C J C. A tutorial on support vector machines for pattern recognition[J]. Data Min Knowl Discov, 1998, 2(2):121-167.
13. PLATT J C, CRISTIANINI N, SHAWE-TAYLOR J. Large margin DAGs for multiclass classification[C]//The Neural Information Processing Systems. Colorado:1999, 12:547-553.
14. GUDMUNDSSON S, RUNARSSON T P, SIGURDSSON S. Automatic sleep staging using support vector machines with posterior probability estimates[C]//2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on Computational Intelligence for Modelling, Control and Automation. Vienna:2005, 2:366-372.

Journal of Biomedical Engineering

Study on Sleep Staging Based on Support Vector Machines and Feature Selection in Single Channel Electroencephalogram

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