北京生物醫(yī)學工程

基于心電信號的睡眠階段的辨識

Sleep-stage identification based on electrocardiogram

作者：董精通張濤林仲志

單位：天津大學電子信息工程學院(天津300072)

關鍵詞：睡眠質(zhì)量；心電圖；心率變異性；類神經(jīng)網(wǎng)絡；時域；頻域

分類號：R318;TP39

出版年·卷·期（頁碼）：2017·36·4（394-399）

摘要：

本研究以睡眠中的心電圖信號為基礎，計算出心率變異性的時域和頻域參數(shù)，根據(jù)自律神經(jīng)系統(tǒng)的變化與心率變異性的關聯(lián)性，對睡眠的不同階段進行辨識，從而實現(xiàn)睡眠質(zhì)量監(jiān)測的居家化。睡眠質(zhì)量辨別算法以監(jiān)督式倒傳遞類神經(jīng)網(wǎng)絡為核心，通過SDNN、RMSSD、SDSD、NN50、pNN50、HF-norm、VLF百分比、5min TP等8個特征值，進行睡眠的5個階段的辨別。實驗通過686組數(shù)據(jù)測試發(fā)現(xiàn)，隱藏層神經(jīng)元數(shù)目為30，性能目標為40，為最佳參數(shù)設定，其中對睡眠中Stage1 階段的識別率可達 93.33%。

The study calculated the parameters of heart rate variability (HRV) in the time domain and frequency domain based on electrocardiogram (ECG) during sleep and established a sleep-stage identification model through the association between the change of the autonomic nervous balance and HRV,which could be integrated with ECG equipment to achieve the sleep quality evaluation in the smart house.The study identified the five stages of sleep with the supervised back-propagation neural network whose eight parameters were SDNN,RMSSD,SDSD,NN50,pNN50,HF-norm,VLF percentage and 5min TP.Through testing 686 groups of data,the results presented that the number of hidden layer neurons as 30,performance goals as 40 were the best parameter setting,and the recognition rate of Stage1 in sleep phase reached 93.33%.

參考文獻：

［1］劉艷驕,孫書臣,洪蘭,等.中國睡眠醫(yī)學的過去、現(xiàn)在與未來［J］.世界睡眠醫(yī)學雜志,2014(1):6-11.

Liu Yanjiao,Sun Shuchen,Hong Lan,et al.Past,present and future of sleep medicine in China［J］.World Journal of Sleep Medicine,2014(1):6-11.

［2］李向新.基于多生理參數(shù)的睡眠分期研究及其應用［D］.天津:天津大學,2012.

［3］Kaplan RF,Wang Y,Loparo KA,et al.Performance evaluation of an automated single-channel sleep-wake detection algorithm［J］.Nature and Science of Sleep,2014,6:113.

［4］Mendez M O,Corthout J,Van Huffel S,et al.Automatic screening of obstructive sleep apnea from the ECG based on empirical mode decomposition and wavelet analysis［J］.Physiological Measurement,2010,31(3):273-289.

［5］吳鋒,俞夢孫,張宏金,等.心率變化特征與睡眠分期耦合關系研究［J］.北京生物醫(yī)學工程,2003,22(3):184-186.

Wu Feng,Yu Mengsun.Zhang Hongjin,et al.Research of correlation between heart rate and sleep phase［J］.Beijing Biomedical Engineering,2003,22(3):184-186.

［6］胡敏,江成璠,王素霞,等.健康人不同睡眠時相心率變異性的差異［J］.中國全科醫(yī)學,2015,18(2):205-207.

Hu Min,Jiang Chengpan,Wang Suxia,et al.Differences of heart rate variability between different sleeping stages in healthy subjects［J］.Chinese General Practice,2015,18(2):205-207.

［7］Wolpert EA.A manual of standardized terminology,techniques and scoring system for sleep stages of human subjects［J］.Archives of General Psychiatry,1969,20(2):246.

［8］Buek P,Vaňková J,Opavsky J,et al.Spectral analysis of heart rate variability in sleep［J］.Physiol Res,2005,54:369-376.

［9］Penzel T,Kantelhardt JW,Grote L,et al.Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea［J］.Biomedical Engineering,IEEE Transactions on,2003,50(10):1143-1151.

［10］Lourakis MIA.A brief description of the Levenberg Marquardt algorithm implemented by levmar［J］.Foundation of Research and Technology,2005,4:1-6.

［11］失眠定義、診斷及藥物治療共識專家組.失眠定義、診斷及藥物治療專家共識(草案)［J］.中華神經(jīng)科雜志,2006,39(2):141-143.

［12］Ichimaru Y,Moody GB.Development of the polysomnographic database on CD-ROM.Psychiatry and Clinical Neurosciences,1999,53(2):175-177.

［13］Goldberger AL,Amaral LAN,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.

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