Novel type of unperturbed sleep monitoring scheme under pillow based on hidden Markov model_Journal of Biomedical Engineering

Authors：

LI Xiang ¹ , LIU Yong ² , CHEN Pengbin ² , WU Jiewei ¹ ,  ZHANG Han ^1,2,3

1. Institute of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, P.R.China;
2. Guangzhou SENVIV Technology Co. Ltd, Guangzhou 510006, P.R.China;
3. Guangdong Provincial Engineering Research Center for Cardiovascular Individual Medicine & Big Data, Guangzhou 510006, P.R.China;

Corresponding author：

ZHANG Han, Email: zhanghan@scnu.edu.cn

Keywords：

sleep stages; heart rate variability; hidden Markov model; ensemble empirical mode decomposition

DOI：

10.7507/1001-5515.201703059

Video：

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Abstract Full text Figures/Tables Video References Cited by

Sleep status is an important indicator to evaluate the health status of human beings. In this paper, we proposed a novel type of unperturbed sleep monitoring system under pillow to identify the pattern change of heart rate variability (HRV) through obtained RR interval signal, and to calculate the corresponding sleep stages combined with hidden Markov model (HMM) under the no-perception condition. In order to solve the existing problems of sleep staging based on HMM, ensemble empirical mode decomposition (EEMD) was proposed to eliminate the error caused by the individual differences in HRV and then to calculate the corresponding sleep stages. Ten normal subjects of different age and gender without sleep disorders were selected from Guangzhou Institute of Respirator Diseases for heart rate monitoring. Comparing sleep stage results based on HMM to that of polysomnography (PSG), the experimental results validate that the proposed noninvasive monitoring system can capture the sleep stages S1–S4 with an accuracy more than 60%, and performs superior to that of the existing sleep staging scheme based on HMM.

Citation： LI Xiang, LIU Yong, CHEN Pengbin, WU Jiewei, ZHANG Han. Novel type of unperturbed sleep monitoring scheme under pillow based on hidden Markov model. Journal of Biomedical Engineering, 2018, 35(2): 280-289. doi: 10.7507/1001-5515.201703059 Copy

1.	Reza B, Foroozan K, Mohammad T. A comparative review on seep stage classification methods in patients and healthy individuals. Comput Methods Programs Biomed, 2016, 19(7): 67-70.
2.	周静, 吴效明. 睡眠呼吸暂停综合征患者脑电的去趋势波动分析. 生物医学工程学杂志, 2016, 33(5): 842-846.
3.	Krenk L, Sørensen G L, Kehlet H, et al. Heart rate response during sleep in elderly patients after fast-track hip and knee arthroplasty. Sleep Biol Rhythms, 2015, 13(3): 229-234.
4.	Mork P J, Nilsson J, Lorås H W, et al. Heart rate variability in fibromyalgia patients and healthy controls during non-REM and REM sleep: a case-control study. Scand J Rheumatol, 2013, 42(6): 505-508.
5.	肖淑媛, 王蓓, 张见, 等. 基于改进的 K 均值聚类算法的睡眠自动分期研究. 生物医学工程学志, 2016, 33(5):847-854.
6.	林秀晶, 夏永明, 钱松荣. 基于支持向量机及特征选择的单通道脑电波睡眠分期研究. 生物医学工程学杂志, 2015, 6(3): 503-507, 513.
7.	李延军, 宏峰, 严洪, 等. 在轨睡眠质量评价的研究进展. 航天医学与医学工程, 2012, 25(6): 458-462.
8.	Redmond S J, Chazal P D, Brien C O, et al. Sleep staging using cardiorespiratory signals. Somnologie, 2007, 11: 245-256.
9.	Aktaruzzaman M, Migliorini M, Tenhunen M, et al. The addition of entropy-based regularity parameters improves sleep stage classification based on heart rate variability. Med Biol Eng Comput, 2015, 53(5): 415-425.
10.	Huang R J, Lai C H, Lee S D, et al. Scaling exponent values as an ordinary function of the ratio of very low frequency to high frequency powers in heart rate variability over various sleep stages. Sleep Breath, 2016, 20(3): 975-985.
11.	Brink M, Müller C H, Schierz C. Contact-free measurement of heart rate, respiration rate, and body movements during sleep. Behav Res Methods, 2006, 38(3): 511-521.
12.	Kortelainen J M, Mendez M O, Bianchi A M, et al. Sleep staging based on signals acquired through bed sensor. IEEE Trans Inf Technol Biomed, 2010, 14(3): 776-785.
13.	庄志, 高上凯, 高小榕. 基于心率变异分析的睡眠分期方法. 生物医学工程学杂志, 2006, 23(3): 499-504.
14.	王金海, 孙微, 韦然. 基于心率变异性分析的睡眠分期研究. 生物医学工程学杂志, 2016, 33(3): 420-425.
15.	Wisse P, Rolf F, Robert H A, et al. Time and state dependent analysis of autonomic control in narcolepsy: higher heart rate with normal heart rate variability independent of sleep fragmentation. Journal of Sleep Research, 2015, 24(2): 206-214.
16.	Ebrahimi F, Setarehdan S K, Nazeran H. DFA- and DWT-based features of HRV signal for automatic sleep staging// Proceedings of the 19th Itanian Conference on Biomedical Engineering. Tehran, Iran: 2016: 21-22.
17.	Muzet A, Werner S, Fuchs G, et al. Assessing sleep architecture and continuity measures through the analysis of heart rate and wrist movement recordings in healthy subjects: comparison with results based on polysomnography. Sleep Med, 2016, 21: 47-56.
18.	Ayappa I, Norman R G. Comparison of limited monitoring using a nasal-cannula flow signal to full polysomnography in sleep-dsordered breathing. Sleep, 2004, 27(6): 1117-1171.
19.	Mendez M O, Matteucci M, Cerutti S, et al. Automatic detection of sleep macrostructure based on bed sensors// Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009. EMBC 2009. Minneapolis, MN, USA: 2009: 1-4.
20.	Wains S A, El-Chami M, Lin H S, et al. Impact of arousal threshold and respiratory effort on the duration of breathing events across sleep stage and time of night. Respir Physiol Neurobiol, 2017, 237: 35-41.
21.	Hall M H, Middleton K, Thayer J F, et al. Racial differences in heart rate variability during sleep in women: the study of women across the nation sleep study. Psychosom Med, 2013, 75(8): 783-790.
22.	Heimann K, Heussen N, Vaeßen P, et al. Basic values for heart and respiratory rates during different sleep stages in healthy infants. Biomed Tech (Berl), 2013, 58(1): 27-34.
23.	郑近德, 程军圣, 杨宇. 改进的 EEMD 算法及其应用研究. 振动与冲击, 2013, 32(21): 21-26, 46.
24.	孔德同, 刘庆超, 雷亚国, 等. 一种改进的 EEMD 方法及其应用研究. 振动工程学报, 2015, 28(6): 1015-1021.
25.	王志堂, 蔡淋波. 隐马尔可夫模型 (HMM) 及其应用. 湖南科技学院学报, 2009, 30(4): 42-44.

1. Reza B, Foroozan K, Mohammad T. A comparative review on seep stage classification methods in patients and healthy individuals. Comput Methods Programs Biomed, 2016, 19(7): 67-70.
2. 周静, 吴效明. 睡眠呼吸暂停综合征患者脑电的去趋势波动分析. 生物医学工程学杂志, 2016, 33(5): 842-846.
3. Krenk L, Sørensen G L, Kehlet H, et al. Heart rate response during sleep in elderly patients after fast-track hip and knee arthroplasty. Sleep Biol Rhythms, 2015, 13(3): 229-234.
4. Mork P J, Nilsson J, Lorås H W, et al. Heart rate variability in fibromyalgia patients and healthy controls during non-REM and REM sleep: a case-control study. Scand J Rheumatol, 2013, 42(6): 505-508.
5. 肖淑媛, 王蓓, 张见, 等. 基于改进的 K 均值聚类算法的睡眠自动分期研究. 生物医学工程学志, 2016, 33(5):847-854.
6. 林秀晶, 夏永明, 钱松荣. 基于支持向量机及特征选择的单通道脑电波睡眠分期研究. 生物医学工程学杂志, 2015, 6(3): 503-507, 513.
7. 李延军, 宏峰, 严洪, 等. 在轨睡眠质量评价的研究进展. 航天医学与医学工程, 2012, 25(6): 458-462.
8. Redmond S J, Chazal P D, Brien C O, et al. Sleep staging using cardiorespiratory signals. Somnologie, 2007, 11: 245-256.
9. Aktaruzzaman M, Migliorini M, Tenhunen M, et al. The addition of entropy-based regularity parameters improves sleep stage classification based on heart rate variability. Med Biol Eng Comput, 2015, 53(5): 415-425.
10. Huang R J, Lai C H, Lee S D, et al. Scaling exponent values as an ordinary function of the ratio of very low frequency to high frequency powers in heart rate variability over various sleep stages. Sleep Breath, 2016, 20(3): 975-985.
11. Brink M, Müller C H, Schierz C. Contact-free measurement of heart rate, respiration rate, and body movements during sleep. Behav Res Methods, 2006, 38(3): 511-521.
12. Kortelainen J M, Mendez M O, Bianchi A M, et al. Sleep staging based on signals acquired through bed sensor. IEEE Trans Inf Technol Biomed, 2010, 14(3): 776-785.
13. 庄志, 高上凯, 高小榕. 基于心率变异分析的睡眠分期方法. 生物医学工程学杂志, 2006, 23(3): 499-504.
14. 王金海, 孙微, 韦然. 基于心率变异性分析的睡眠分期研究. 生物医学工程学杂志, 2016, 33(3): 420-425.
15. Wisse P, Rolf F, Robert H A, et al. Time and state dependent analysis of autonomic control in narcolepsy: higher heart rate with normal heart rate variability independent of sleep fragmentation. Journal of Sleep Research, 2015, 24(2): 206-214.
16. Ebrahimi F, Setarehdan S K, Nazeran H. DFA- and DWT-based features of HRV signal for automatic sleep staging// Proceedings of the 19th Itanian Conference on Biomedical Engineering. Tehran, Iran: 2016: 21-22.
17. Muzet A, Werner S, Fuchs G, et al. Assessing sleep architecture and continuity measures through the analysis of heart rate and wrist movement recordings in healthy subjects: comparison with results based on polysomnography. Sleep Med, 2016, 21: 47-56.
18. Ayappa I, Norman R G. Comparison of limited monitoring using a nasal-cannula flow signal to full polysomnography in sleep-dsordered breathing. Sleep, 2004, 27(6): 1117-1171.
19. Mendez M O, Matteucci M, Cerutti S, et al. Automatic detection of sleep macrostructure based on bed sensors// Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009. EMBC 2009. Minneapolis, MN, USA: 2009: 1-4.
20. Wains S A, El-Chami M, Lin H S, et al. Impact of arousal threshold and respiratory effort on the duration of breathing events across sleep stage and time of night. Respir Physiol Neurobiol, 2017, 237: 35-41.
21. Hall M H, Middleton K, Thayer J F, et al. Racial differences in heart rate variability during sleep in women: the study of women across the nation sleep study. Psychosom Med, 2013, 75(8): 783-790.
22. Heimann K, Heussen N, Vaeßen P, et al. Basic values for heart and respiratory rates during different sleep stages in healthy infants. Biomed Tech (Berl), 2013, 58(1): 27-34.
23. 郑近德, 程军圣, 杨宇. 改进的 EEMD 算法及其应用研究. 振动与冲击, 2013, 32(21): 21-26, 46.
24. 孔德同, 刘庆超, 雷亚国, 等. 一种改进的 EEMD 方法及其应用研究. 振动工程学报, 2015, 28(6): 1015-1021.
25. 王志堂, 蔡淋波. 隐马尔可夫模型 (HMM) 及其应用. 湖南科技学院学报, 2009, 30(4): 42-44.

Journal of Biomedical Engineering

Novel type of unperturbed sleep monitoring scheme under pillow based on hidden Markov model

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