Research Progress of Automatic Sleep Staging Based on Electroencephalogram Signals_Journal of Biomedical Engineering

Authors：

GAOQunxia , ZHOUJing ,  WUXiaoming

School of Materials Science and Engineering, South China University of Technology, Guangzhou 510640, China;

Corresponding author：

WUXiaoming, Email: bmxmwus@scut.edu.cn

Keywords：

automatic sleep staging; electroencephalogram; feature extraction; pattern recognition

DOI：

10.7507/1001-5515.20150205

Video：

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The research of sleep staging is not only a 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 hot spot and got some achievements. The basic knowledge of sleep staging and electroencephalogram (EEG) is introduced in this paper. Then, feature extraction and pattern recognition, two key technologies for automatic sleep staging, are discussed in detail. Wavelet transform and Hilbert-Huang transform, two methods for feature extraction, are compared. Artificial neural network and support vector machine (SVM), two methods for pattern recognition are discussed. In the end, the research status of this field is summarized, and development trends of next phase are pointed out.

Citation： GAOQunxia, ZHOUJing, WUXiaoming. Research Progress of Automatic Sleep Staging Based on Electroencephalogram Signals. Journal of Biomedical Engineering, 2015, 32(5): 1155-1159. doi: 10.7507/1001-5515.20150205 Copy

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2.	HAMIDA S T B, AHMED B. Computer based sleep staging:Challenges for the future[C]//20137th IEEE GCC Conference and Exhibition (GCC). Doha:2013:280-285.
3.	RECHTSCHAFFEN A, KALES A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects[M]. Los Angeles, CA:Brain Information Service/Brain Research Institute, University of California:1968.
4.	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.
5.	DARKHOVSKY B, PIRYATINSKA A. A new complexity-based algorithmic procedures for electroencephalogram (EEG) segmentation[C]//2012 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). New York, NY:2012:1-5.
6.	ERDAMAR A, DUMAN F, YETKIN S. A wavelet and teager energy operator based method for automatic detection of K-Complex in sleep EEG[J]. Expert Syst Appl, 2012, 39(1):1284-1290.
7.	CHOUVARDA I, ROSSO V, MENDEZ M O, et al. Assessment of the EEG complexity during activations from sleep[J]. Comput Methods Programs Biomed, 2011, 104(3):e16-e28.
8.	LI Xiaoxia, XU Guizhi, SHANG Xiukui, et al. Correlation dimension analysis of EEG signals induced by electrical acupuncture[C]//20136th International Conference on Biomedical Engineering and Informatics (BMEI). Hangzhou:2013:585-590.
9.	KUMAR Y, DEWAL M L, ANAND R S. Features extraction of EEG signals using approximate and sample entropy[C]//2012 IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS). Bhopal, 2012:1-5.
10.	LIANG Shengfu, KUO C E, HU Y H, et al. Automatic stage scoring of single-channel sleep EEG by using multiscale entropy and autoregressive models[J]. IEEE Trans Instrum Meas, 2012, 61(6):1649-1657.
11.	AYDIN S, SARAOĞGLU H M, KARA S. Singular spectrum analysis of sleep EEG in insomnia[J]. J Med Syst, 2011, 35(4):457-461.
12.	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.
13.	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.
14.	ESTRADA E, NAZERAN H, SIERRA G, et al. Wavelet-based EEG denoising for automatic sleep stage classification[C]//201121st International Conference on Electrical Communications and Computers (CONIELECOMP). San Andres Cholula:2011:295-298.
15.	AZIM M R, AMIN M S, HAQUE S A, et al. Feature extraction of human sleep EEG signals using wavelet transform and Fourier transform[C]//20102nd International Conference on Signal Processing Systems (ICSPS). Dalian:2010, 3:V3-701-V3-705.
16.	KHALIGHI S, SOUSA T, OLIVEIRA D, et al. Efficient feature selection for sleep staging based on maximal overlap discrete wavelet transform and SVM[C]//2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC. Boston, MA:2011:3306-3309.
17.	FRAIWAN L, LWEESY K, KHASAWNEH N, et al. Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier[J]. Comput Methods Programs Biomed, 2012, 108(1):10-19.
18.	CHANG Naifu, CHEN T C, CHIANG C Y, et al. On-line empirical mode decomposition biomedical microprocessor for Hilbert Huang transform[C]//2011 IEEE Biomedical Circuits and Systems Conference (BioCAS). San Diego, CA:2011:420-423.
19.	LIN C F, ZHU Jinde. Hilbert-Huang transformation-based time-frequency analysis methods in biomedical signal applications[J]. Proc Inst Mech Eng H, 2012, 226(3):208-216.
20.	SHEN Xueli, FAN Yingle. Sleep stage classification based on EEG signals by using improved Hilbert-Huang transform[J]. Appl Mech Mater, 2011, 138-139:1096-1101.
21.	LIU Yuelei, YAN Lanfeng, ZENG Bo, et al. Automatic sleep stage scoring using Hilbert-Huang transform with BP neural network[C]//20104th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). Chengdu:2010:1-4.
22.	ZHU Xiaojun, LV Shiqin, FAN Liujuan, et al. The EEG signal process based on EEMD[C]//20112nd International Symposium on Intelligence Information Processing and Trusted Computing (IPTC). Wuhan:2011:222-225.
23.	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.
24.	RONZHINA M, JANOUŠEK O, KOLÁÁŘOVÁ J, et al. Sleep scoring using artificial neural networks[J]. Sleep Med Rev, 2012, 16(3):251-263.
25.	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.
26.	SEE A R, 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.
27.	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.
28.	KEMPFNER J, JENNUM P, SORENSEN H B D, et al. Automatic SLEEP staging:From young aduslts to elderly patients using multi-class support vector machine[C]//201335th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Osaka:2013:5777-5780.
29.	LEE J E, YOO S K. Electroencephalography analysis using neural network and support vector machine during sleep[J]. Engineering, 2013, 5(5B):88-92.
30.	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.

1. KRYGER M H, ROTH T, DEMENT W C. Principles and practice of sleep medicine:expert consult premium edition-enhanced online features[M]. 5th ed. Philadelphia:Saunders/Elsevier, 2011.
2. HAMIDA S T B, AHMED B. Computer based sleep staging:Challenges for the future[C]//20137th IEEE GCC Conference and Exhibition (GCC). Doha:2013:280-285.
3. RECHTSCHAFFEN A, KALES A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects[M]. Los Angeles, CA:Brain Information Service/Brain Research Institute, University of California:1968.
4. 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.
5. DARKHOVSKY B, PIRYATINSKA A. A new complexity-based algorithmic procedures for electroencephalogram (EEG) segmentation[C]//2012 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). New York, NY:2012:1-5.
6. ERDAMAR A, DUMAN F, YETKIN S. A wavelet and teager energy operator based method for automatic detection of K-Complex in sleep EEG[J]. Expert Syst Appl, 2012, 39(1):1284-1290.
7. CHOUVARDA I, ROSSO V, MENDEZ M O, et al. Assessment of the EEG complexity during activations from sleep[J]. Comput Methods Programs Biomed, 2011, 104(3):e16-e28.
8. LI Xiaoxia, XU Guizhi, SHANG Xiukui, et al. Correlation dimension analysis of EEG signals induced by electrical acupuncture[C]//20136th International Conference on Biomedical Engineering and Informatics (BMEI). Hangzhou:2013:585-590.
9. KUMAR Y, DEWAL M L, ANAND R S. Features extraction of EEG signals using approximate and sample entropy[C]//2012 IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS). Bhopal, 2012:1-5.
10. LIANG Shengfu, KUO C E, HU Y H, et al. Automatic stage scoring of single-channel sleep EEG by using multiscale entropy and autoregressive models[J]. IEEE Trans Instrum Meas, 2012, 61(6):1649-1657.
11. AYDIN S, SARAOĞGLU H M, KARA S. Singular spectrum analysis of sleep EEG in insomnia[J]. J Med Syst, 2011, 35(4):457-461.
12. 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.
13. 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.
14. ESTRADA E, NAZERAN H, SIERRA G, et al. Wavelet-based EEG denoising for automatic sleep stage classification[C]//201121st International Conference on Electrical Communications and Computers (CONIELECOMP). San Andres Cholula:2011:295-298.
15. AZIM M R, AMIN M S, HAQUE S A, et al. Feature extraction of human sleep EEG signals using wavelet transform and Fourier transform[C]//20102nd International Conference on Signal Processing Systems (ICSPS). Dalian:2010, 3:V3-701-V3-705.
16. KHALIGHI S, SOUSA T, OLIVEIRA D, et al. Efficient feature selection for sleep staging based on maximal overlap discrete wavelet transform and SVM[C]//2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC. Boston, MA:2011:3306-3309.
17. FRAIWAN L, LWEESY K, KHASAWNEH N, et al. Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier[J]. Comput Methods Programs Biomed, 2012, 108(1):10-19.
18. CHANG Naifu, CHEN T C, CHIANG C Y, et al. On-line empirical mode decomposition biomedical microprocessor for Hilbert Huang transform[C]//2011 IEEE Biomedical Circuits and Systems Conference (BioCAS). San Diego, CA:2011:420-423.
19. LIN C F, ZHU Jinde. Hilbert-Huang transformation-based time-frequency analysis methods in biomedical signal applications[J]. Proc Inst Mech Eng H, 2012, 226(3):208-216.
20. SHEN Xueli, FAN Yingle. Sleep stage classification based on EEG signals by using improved Hilbert-Huang transform[J]. Appl Mech Mater, 2011, 138-139:1096-1101.
21. LIU Yuelei, YAN Lanfeng, ZENG Bo, et al. Automatic sleep stage scoring using Hilbert-Huang transform with BP neural network[C]//20104th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). Chengdu:2010:1-4.
22. ZHU Xiaojun, LV Shiqin, FAN Liujuan, et al. The EEG signal process based on EEMD[C]//20112nd International Symposium on Intelligence Information Processing and Trusted Computing (IPTC). Wuhan:2011:222-225.
23. 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.
24. RONZHINA M, JANOUŠEK O, KOLÁÁŘOVÁ J, et al. Sleep scoring using artificial neural networks[J]. Sleep Med Rev, 2012, 16(3):251-263.
25. 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.
26. SEE A R, 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.
27. 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.
28. KEMPFNER J, JENNUM P, SORENSEN H B D, et al. Automatic SLEEP staging:From young aduslts to elderly patients using multi-class support vector machine[C]//201335th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Osaka:2013:5777-5780.
29. LEE J E, YOO S K. Electroencephalography analysis using neural network and support vector machine during sleep[J]. Engineering, 2013, 5(5B):88-92.
30. 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.

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