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Sleep stage detection using only heart rate (本文)

満倉, 靖恵 慶應義塾大学

2020.09.07

概要

Getting enough quality sleep plays a vital role in protecting our mental health, physical health, and quality of life. Sleep deprivation can make it difficult to concentrate on daily activities, and lower sleep quality is associated with hypertension, hyperglycemia, and hyperlipidemia. The amount of sleep we get is important, but in recent years, quality sleep has also been deemed significant. Polysomnography, which has been the gold standard in assessing sleep quality based on stages, requires that the subject be attached to electrodes, which can disrupt sleep. An easier method to objectively measure sleep is therefore needed. The aim of this study was to construct an easy and objective sleep stage monitoring method. A cross-sectional study for healthy subjects has been done in our research. A new easy model for monitoring the sleep stages is built on only heart rate calculated by the electrocardiogram. This enabled us to easily assess the sleep quality based on five stages. This experiment included a total of 50 subjects. The overall accuracy in determining the five sleep stages was 66.0 percent. Four stages for sleep are identified accurately compared with other conventional methods. Despite there are no five sleep stage separation method using only heart rate, our method achieved the five separation for sleep with a relatively good accuracy. This study represents a great contribution to the field of sleep science. Because sleep stages can be recognized by the heart rate alone, sleep can be noninvasively assessed with any heart rate meter. This method will make it easier to determine sleep stages and diagnose sleep disorders.

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参考文献

1. Assoumou HGN, Gaspoz JM, Sforza E, et al. Obstructive sleep apnea syndrome is associated with some components of metabolic syndrome. Sleep Breath 2012; 16(2): 895–902.

2. Rechtschaffen A and Kales A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Washington, DC: US Department of Health, Education, and Welfare, 1968.

3. Silber MH, Ancoli-Israel S, Bonnet MH, et al. The visual scoring of sleep in adults. J Clin Sleep Med 2007; 3(2): 121–131.

4. Iber C, Ancoli-Israel S, Chesson AL, et al. The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications. 1st ed. Westchester, IL: American Academy of Sleep Medicine, 2007.

5. Liang SF, Kuo CE, Pan YH, et al. Automatic stage scoring of single-channel sleep EEG by using multi- scale entropy and autoregressive models. IEEE T Instrum Meas 2012; 61(6): 1649–1657.

6. Zhu G, Li Y and Wen P. Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal. IEEE J Biomed Health Inform 2014; 18(6): 1813–1821.

7. Singh J, Sharma RK and Gupta AK. A method of REM-NREM sleep distinction using ECG signal for unobtrusive personal monitoring. Comput Biol Med 2016; 78: 138–143.

8. Scholz UJ, Bianchi AM, Cerutti S, et al. Vegetative background of sleep: spectral analysis of the heart rate variability. Physiol Behav 1997; 62(5): 1037–1043.

9. Boudreau P, Yeh WH, Dumont GA, et al. Circadian variation of heart rate variability across sleep stages. Sleep 2013; 36(12): 1919–1928.

10. Penzel T, Kantelhardt JW, Bartsch RP, et al. Modulations of heart rate, ECG and cardio-respiratory coupling observed in polysomnography. Front Physiol 2016; 7: 460–415.

11. Trinder J, Kleiman J, Carrington M, et al. Autonomic activity during human sleep as a function of time and sleep stage. J Sleep Res 2001; 10(4): 253–264.

12. Togo F and Yamamoto Y. Decreased fractal component of human heart rate variability during non-REM sleep. Am J Physiol Heart Circ Physiol 2001; 280(1): H17–H21.

13. Townsend RE, Prinz PN and Obrist WD. Human cerebral blood flow during sleep and waking. J Appl Physiol 1973; 35(5): 620–625.

14. Dumont M, Jurysta F, Lanquart J, et al. Interdependency between heart rate variability and sleep EEG: linear/non-linear. Clin Neurophysiol 2004; 115(9): 2031–2040.

15. Yeh JR, Peng CK, Lo MT, et al. Investigating the interaction between heart rate variability and sleep EEG using nonlinear algorithms. J Neurosci Methods 2013; 219(2): 233–239.

16. Akaike H. Fitting autoregressive models for prediction. Ann Inst Stat Math 1969; 21(1): 243–247.

17. Hamilton JD. Time series analysis. Princeton, NJ: Princeton University Press, 1994.

18. Akaike H. A new look at the statistical identification model. IEEE T Automat Control 1974; 19(6): 716–723.

19. Elman JL. Finding structure in time. Cogn Sci 1990; 14(2): 179–211.

20. Baum LE and Petrie T. Statistical Inference for probabilistic functions of finite state Markov chains. Ann Math Stat 1966; 37(6): 1554–1563.

21. Lippmann R. An introduction to computing with neural nets. IEEE ASSP Mag 1987; 4(2): 4–22.

22. Hsu CW, Chang CC and Lin CJ. A practical guide to support vector classification, 2003, pp.1–16, https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

23. Breiman L. Random forests. Mach Learn 2001; 45(1): 5–32.

24. Kohonen T. Self-organizing formation of topologically correct feature maps. Biol Cybernet 1982; 43: 59–69.

25. Viterbi AJ. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE T Inform Theory 1967; 13(2): 260–269.

26. Somers VK, Dyken ME, Mark AL, et al. Sympathetic nerve activity during sleep in normal subjects. N Engl J Med 1993; 328(5): 303–307.

27. Mendez M, Bianchi AM, Villantieri O, et al. Time-varying analysis of the heart rate variability during REM and Non-REM sleep stages. Conf Proc IEEE Eng Med Biol Soc 2006; 1: 3576–3579.

28. Yoshimoto M, Yoshida I and Miki K. Functional role of diverse changes in sympathetic nerve activity in regulating arterial pressure during REM sleep. Sleep 2011; 34(8): 1093–1101.

29. Busek P, Vankov J, Opavsk J, et al. Spectral analysis of the heart rate variability in sleep. Physiol Res 2004; 54(4): 369–376.

30. Tanida K, Shibata M and Heitkemper MM. Sleep stage assessment using power spectral indices of heart rate variability with a simple algorithm: limitations clarified from preliminary study. Biol Res Nurs 2013; 15(3): 264–272.

31. Fonseca P, Long X, Radha M, et al. Sleep stage classification with ECG and respiratory effort. Physiol Meas 2015; 36(10): 2027–2040.

32. Willemen T, Van Deun D, Verhaert V, et al. An evaluation of cardiorespiratory and movement features with respect to sleep-stage classification. IEEE J Biomed Health Inform 2014; 18(2): 661–669.

33. Isa SM, Wasito I and Arymurthy AM. Kernel dimensionality reduction on sleep stage classification using ECG signal. Int J Comput Sci Iss 2011; 8(2): 115–123.

34. Martinez D, Lumertz MS and Lenz Mdo C. Dimensions of sleepiness and their correlations with sleep- disordered breathing in mild sleep apnea. J Bras Pneumol 2009; 35(6): 507–514.

35. Wei Y, Colombo MA, Ramautar JR, et al. Sleep stage transition dynamics reveal specific stage 2 vulner- ability in insomnia. Sleep 2017; 40(9): zsx117.

36. Herlan A, Ottenbacher J, Schneider J, et al. Electrodermal activity patterns in sleep stages and their util- ity for sleep versus wake classification. J Sleep Res 2018; 1: e12694.

37. Qanash S, Giannouli E and Younes M. Assessment of intervention-related changes in non-rapid-eye- movement sleep depth: importance of sleep depth changes within stage 2. Sleep Med 2017; 40: 84–93.

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