Automatic Sleep Staging Method Based on Energy Features and Least Squares Support Vector Machine Classifier_Journal of Biomedical Engineering

Authors：

GAOQunxia ¹ ,  ZHOUJing ¹ , YEBinggang ² , WUXiaoming ¹

1. School of Materials Science and Engineering, South China University of Technology, Guangzhou 510640, China;
2. Guangdong Food and Drug Vocational College, Guangzhou 510520, China;

Corresponding author：

ZHOUJing, Email: hellozj@scut.edu.cn

Keywords：

automatic sleep staging; single-channel electroencephalogram; energy feature; least squares support vector machine; FIR band-pass filter; wavelet packet transform

DOI：

10.7507/1001-5515.20150097

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The research of sleep staging is not only the basis of diagnosing sleep related diseases, but also the precondition of evaluating sleep quality, and has important clinical significance. In recent years, the research of automatic sleep staging based on computer has become a hotspot and made some achievements. Feature extraction and feature classification are two key technologies in automatic sleep staging system. In order to achieve effective automatic sleep staging, we proposed a new automatic sleep staging method which combines the energy features and least squares support vector machines (LS-SVM). Firstly, we used FIR band-pass filter to extract the energy features of Pz-Oz channel sleep electroencephalogram (EEG) signals, and compared them with those from wavelet packet transform method. Then we designed an LS-SVM classifier to realize the automatic sleep stage classification. The research showed that FIR band-pass filter (with the Kaiser window) performed better than wavelet packet transform (WPT) for energy feature extraction just in terms of the data from the Sleep-EDF Database and the LS-SVM classifier (with the RBF Kernel function) designed was good, and the automatic sleep staging method proposed in this paper was better than many similar methods from other studies with an average accuracy of 88.89% and had a very prosperous application future.

Citation： GAOQunxia, ZHOUJing, YEBinggang, WUXiaoming. Automatic Sleep Staging Method Based on Energy Features and Least Squares Support Vector Machine Classifier. Journal of Biomedical Engineering, 2015, 32(3): 531-536. doi: 10.7507/1001-5515.20150097 Copy

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1. HAMIDA S T B, AHMED B. Computer based sleep staging:Challenges for the future[C]//2013 7th IEEE GCC Conference and Exhibition (GCC). Doha:2013:280-285.
2. MOTAMEDI-FAKHR S, MOSHREFI-TORBATI M, HILL M, et al. Signal processing techniques applied to human sleep EEG signals-A review[J]. Biomed Signal Process Control, 2014, (10):21-33.
3. KOLEY B, DEY D. An ensemble system for automatic sleep stage classification using single Channel EEG signal[J]. Comput Biol Med, 2012, 42(12):1186-1195.
4. 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.
5. RONZHINA M, JANOUŞEK O, KOLÁŘOVÁ J, et al. Sleep scoring using artificial neural networks[J]. Sleep Med Rev, 2012, 16(3):251-263.
6. HSU Y L, YANG Y T, WANG J S, et al. Automatic sleep stage recurrent neural classifier using energy features of EEG signals[J]. Neurocomputing, 2013, 104:105-114.
7. SEE R A, LIANG C K. A study on sleep EEG Using sample entropy and power spectrum analysis[C]//2011 Defense Science Research Conference and Expo (DSR). Singapore:2011:1-4.
8. HUANG C S, LIN C L, KO L W, et al. A hierarchical classification system for sleep stage scoring via forehead EEG signals[C]//2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB). Singapore:2013:1-5.
9. KEMPFNER J, JENNUM P, SORENSEN H B D, et al. Automatic sleep staging:From young adults to elderly patients using multi-class support vector machine[C]//2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Osaka:2013:5777-5780.
10. BAJAJ V, PACHORI R B. Automatic classification of sleep stages based on the time-frequency image of EEG signals[J]. Comput Methods Programs Biomed, 2013, 112(3):320-328.
11. RECHTSCHAFFEN A, KALES A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects[M]. Los Angeles:Brain Information Service, 1968.
12. IBER C, ANCOLI-ISRAEL S, CHESSON A, et al. The AASM manual for the scoring of sleep and associated events:rules, terminology and technical specifications[M]. Westchester:American Academy of Sleep Medicine:2007.
13. PARK H J, OH J S, JEONG D U, et al. Automated sleep stage scoring using hybrid rule-and case-based reasoning[J]. Comput Biomed Res, 2000, 33(5):330-349.
14. LEE J E, YOO S K. Electroencephalography analysis using neural network and support vector machine during sleep[J]. Engineering, 2013, 5(5B):88-92.
15. 孙延奎.小波分析及其应用[M].北京:机械工业出版社, 2005:1-285.
16. SUYKENS J A K, VANDEWALLE J. Least squares support vector machine classifiers[J]. Neural Process Lett, 1999, 9(3):293-300.
17. SUYKENS J A K, VAN GESTEL T, DE BRABANTER J, et al. Least squares support vector machines[M]. Singapore:World Scientific, 2002:249-294.
18. GOLDBERGER A L, AMARAL L A, GLASS L, et al. PhysioBank, PhysioToolkit, and PhysioNet:components of a new research resource for complex physiologic signals[J]. Circulation, 2000, 101(23):E215-E220.
19. KEMP B, ZWINDERMAN AH, TUK B, et al. Analysis of a sleep dependent neuronal feedback loop:the slow-wave microcontinuity of the EEG[J]. IEEE Trans Biomed Eng, 2000, 47(9):1185-1194.
20. FRAIWAN L A, KHASWANEH N Y, LWEESY K Y. Automatic sleep stage scoring with wavelet packets based on single EEG recording[J]. International Science Index, 2009, 3(6):416-419.
21. PASCUALVACA J M S, FERNANDES C M, GUILLÉN A, et al. Sleep stage classification using advanced intelligent methods[M]//ROJAS I, JOYA G, GABESTANY J. Advances in Computational Intelligence. Berlin:Springer, 2013, 7902:604-612.

Journal of Biomedical Engineering

Automatic Sleep Staging Method Based on Energy Features and Least Squares Support Vector Machine Classifier

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