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| Study on Sleep Staging Algorithm Based on EEG Signals |
1 School of Electrical Engineering and Automatic, Harbin Institute of Technology, Harbin 150001, China
2 Medical Devices Co. Ltd. Hainan HeMei, Haikou 570125, China
3 First Affiliated Hospital of Harbin Medical University, Harbin 150001, China |
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Abstract The quality of sleep is closely related to the human life. Monitoring sleep quality accurately can play an effective role in helping people improve the quality of sleep. We chose the EEG and sleep state data of slp01, slp02 and slp04 samples of MIT-BIH Polysomnographic database as the analysis object, use the wavelet transform of ‘sym7’ with 7 layers decomposition to denoise the EEG signal, and extract the symbolic entropy, the detrended fluctuation index and the delta frequency band energy ratio through the nonlinear analysis of symbolic dynamics, detrended fluctuation analysis and spectrum analysis. Besides, the calibration samples and prediction samples of each sample were established according to the proportion of 4 to 1 by KennardStone method, and the sleep staging are realized by the least squares support vector machine (LS-SVM). Results demonstrated that the three parameters were highly correlated to the sleep state, and the correlation coefficients of them to the sleep state were higher than 083, the embedding dimension and time delay of the symbol entropy parameters are 4 and 1, and the interval of detrended fluctuation was 30-500, the mean of sleep staging accuracy reached 9287%. The accuracy improved about 5% compared to the complexity and approximate entropy algorithm.
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［1］严由伟，刘明艳，唐向东，等. 压力源及其与睡眠质量的现象学关系研究述评［J］. 心理科学进展，2010，18（10）：1537-1547.
［2］王琛磊. 基于DSP的睡眠监测系统设计与实现［D］. 广州：华南理工大学，2013.
［3］于立群，高小榕，刘伟国，等. 基于脑电的睡眠与麻醉中失觉醒脑状态分析［J］. 清华大学学报(自然科学版)，2009，49（12）： 2013-2016.
［4］Lin CT，Chuang CH，Huang CS，et al. Wireless and wearable EEG system for evaluating driver vigilance ［J］. IEEE Trans Biomed Circuits Syst，2014，8（2）：165-176.
［5］彭振，韦明，郭建平，等. 基于奇异值第一主成分的睡眠脑电分期方法研究［J］. 现代生物医学进展，2014，14（07）：1368-1372.
［6］王歆媛，汪丰. 基于EEG复杂度和近似熵的睡眠自动分期［J］. 软件，2013，34（02）：97-100.
［7］江朝晖，李继伟，冯焕清，等. RR间期分析与睡眠分期［J］. 生物医学工程研究，2003，22（03）：17-20.
［8］马千里，卞春华，王俊. 脑电信号的标度分析及其在睡眠状态区分中的应用［J］. 物理学报，2010，59（7）：4480-4484.
［9］Syed AI， Esther RV. A low computational cost algorithm for REM sleep detection using single channel EEG ［J］. Ann Biomed Eng，2014，42(11）：2344-2359.
［10］Ichimaru Y，Moody GB. Development of the polysomnographic database on CDROM ［J］. Psychiat Clin Neuros，1999，53（2）：175-177.
［11］Lin Peifeng，Tsao J，Lo MT，el al. Symbolic entropy of the amplitude rather than the instantaneous frequency of EEG varies in dementia ［J］. Entropy，2015，17（2）：560-579.
［12］刘函林，黄华，刘圹彬. 脑电信号分析的实用符号动力学方法研究［J］. 生物医学工程学杂志，2010，27（02）：407-410.
［13］Jospin M，Caminal P，Jensen EW，et al. Detrended fluctuation analysis of EEG as a measure of depth of anesthesia ［J］. IEEE Trans Biomed Eng，2007，54(5)：840-846.
［14］Li Hua，Wang Juxiang，Xin Zhina，et al. Influence of improved kennard/stone algorithm on the calibration transfer in nearinfrared spectroscopy ［J］. Spectrosc Spect Anal，2011，31（2）：362-365.
［15］Suykens JAK，Vandewalle J. Least squares support vector machine classifiers ［J］. Neural Processing Letter，1999，9（3）：293-300.
［16］Lee JM，Kim DJ，Kim IY，et al. Detrended fluctuation analysis of EEG in sleep apnea using MIT/BIH polysomnography data ［J］. Comput Biol Med，2002，32（1）：37-47.
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