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
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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