Study on Sleep Staging Methods Based on Heart Rate Variability Analysis_Journal of Biomedical Engineering

Authors：

WANGJinhai ^1,2 , SUNWei ¹ ,  WEIRan ^1,2 , ZHAOXiaoyun ³ , GUOHaiding ¹ , WANGHuiquan ^1,2

1. School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China;
2. Tianjin Medical Electronic Treating-Technology Engineering Center, Tianjin 300387, China;
3. Tianjin Thoracic Hospital, Tianjin 300350, China;

Corresponding author：

WEIRan, Email: weiran@tjpu.edu.cn

Keywords：

heart rate variability; principal component analysis; support vector machine; sleep staging

DOI：

10.7507/1001-5515.20160071

Video：

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Abstract Full text Figures/Tables Video References Cited by

In order to realize sleep staging automatically and conveniently, we used support vector machine (SVM) to analyze the correlation between heart rate variability and sleep stage experimentally. R-R intervals (RRIs) from 33 cases of sleep clinical data of Tianjin Thoracic Hospital were extracted and analyzed by principal component analysis (PCA). The SVM method was used to establish the model and predict the five sleep stages. The prediction accuracy of three-sleep-stage was higher than 80%, in contrast to sleep scoring annotations marked by physiological experts based on electroencephalogram (EEG) golden standard. The result showed that there was a good correlation between heart rate variability and sleep staging. This method is an important supplement to the traditional sleep staging method and has a great value for clinical application.

Citation： WANGJinhai, SUNWei, WEIRan, ZHAOXiaoyun, GUOHaiding, WANGHuiquan. Study on Sleep Staging Methods Based on Heart Rate Variability Analysis. Journal of Biomedical Engineering, 2016, 33(3): 420-425. doi: 10.7507/1001-5515.20160071 Copy

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25.	艾娜, 吴作伟,任江华.支持向量机与人工神经网络[J].山东理工大学学报:自然科学版,2005,19(5) :45-49.

1. GJORESKI H, RASHKOVSKA A, KOZINA S A, et al. Telehealth using ECG sensor and accelerometer[C]//201437th International Convention on Information and Communication Technology, Electronics And Microelectronics (MIPRO). Opatija:2014:270-274.
2. LEE J, JUNG J, LEE J, et al. Diagnostic device for acute cardiac disease using ECG and accelerometer[C]//2014 International Conference on Information Science and Applications (ICISA). Seoul:2014:1-3.
3. KABIR M A, SHAHNAZ C. Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains[J]. Biomed Signal Process Control, 2012, 7(5) :481-489.
4. RECHTSCHAFFEN A, KALES A A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects[M]. Washington:U.S. Dept. Health, Education and Welfare, 1968.
5. 张宏金, 杨军,俞梦孙,等.微动敏感床垫式睡眠监测系统与多导睡眠图的比较研究[J].实用诊断与治疗杂志,2004,18(6) :476-478.
6. ZHAO Zhidong, MA Chan. A novel cancellation method of power line interference in ECG signal based on EMD and adaptive filter[C]//11th IEEE International Conference on Communication Technology, 2008. ICCT 2008. Hangzhou:2008:517-520.
7. AGRAWAL S, GUPTA A. Fractal and EMD based removal of baseline wander and powerline interference from ECG signals[J]. Comput Biol Med, 2013, 43(11) :1889-1899.
8. PAN J, TOMPKINS W J. A real-time QRS detection algorithm[J]. IEEE Trans Biomed Eng, 1985, 32(3) :230-236.
9. 李延军, 宏峰,严洪,等.在轨睡眠质量评价的研究进展[J].航天医学与医学工程,2012,25(6) :458-462.
10. CAFFAREL J, GIBSON G J, HARRISON J P, et al. Comparison of manual sleep staging with automated neural network-based analysis in clinical practice[J]. Med Biol Eng Comput, 2006, 44(1-2) :105-110.
11. KIRSCH M R, MONAHAN K, WENG J, et al. Entropy-based measures for quantifying sleep-stage transition dynamics:relationship to sleep fragmentation and daytime sleepiness[J]. IEEE Trans Biomed Eng, 2012, 59(3) :787-796.
12. JIA R T, LIU B. Human daily activity recognition by fusing accelerometer and multi-lead ECG data[C]//2013 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC). Kunming:2013:1-4.
13. ZHANG Zhengbo, Silva I, WU Dalei, et al. Adaptive motion artefact reduction in respiration and ECG signals for wearable healthcare monitoring systems[J]. Medical & Biological Engineering & Computing, 2014, 52(12) :1019-1030.
14. ALTIMIRAS J. Understanding autonomic sympathovagal balance from short-term heart rate variations. Are we analyzing noise?[J] Comparative Biochemistry and Physiology Part A, 1999, 124(4) :447-460.
15. BOOTSMA M, SWENNE C A, VAN BOLHUIS H H, et al. Heart rate and heart rate variability as indexes of sympathovagal balance[J]. Am J Physiol, 1994, 266(4 Pt 2) :H1565-H1571.
16. GOLDBERGER A L. Is the normal heartbeat chaotic or homeostatic?[J]. News Physiol Sci, 1991, 6:87-91.
17. VANOLI E, ADAMSON P B, BA-LIN, et al. Heart rate variability during specific sleep stages. A comparison of healthy subjects with patients after myocardial infarction[J]. Circulation, 1995, 91(7) :1918-1922.
18. BRANDENBERGER G, EHRHART J, PIQUARD F, et al. Inverse coupling between ultradian oscillations in delta wave activity and heart rate variability during sleep[J]. Clin Neurophysiol, 2001, 112(6) :992-996.
19. OTZENBERGER H, SIMON C, GRONFIER C, et al. Temporal relationship between dynamic heart rate variability and electroencephalographic activity during sleep in man[J]. Neurosci Lett, 1997, 229(3) :173-176.
20. ALDREDGE J L, WELCH A J. Variations of heart rate during sleep as a fucntion of the sleep cycle[J]. Electroencephalogr Clin Neurophysiol, 1973, 35(2) :193-198.
21. 江朝晖, 李继伟,冯焕清,等.R-R间期分析与睡眠分期[J].生物医学工程研究,2003,22(3) :17-20.
22. 庄志, 高上凯,高小榕.基于心率变异分析的睡眠分期方法[J].生物医学工程学杂志,2006,23(3) :499-504.
23. 尚志刚, 张建华.生物医学数据分析及其MATLAB实现[M].北京:北京大学出版社,2009.
24. 张学工. 基于统计学习理论的支持向量机算法研究[J].自动化学报,2000,78(1) :32-42.
25. 艾娜, 吴作伟,任江华.支持向量机与人工神经网络[J].山东理工大学学报:自然科学版,2005,19(5) :45-49.

Journal of Biomedical Engineering

Study on Sleep Staging Methods Based on Heart Rate Variability Analysis

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