Study on the prediction of cardiovascular disease based on sleep heart rate variability analysis_Journal of Biomedical Engineering

Authors：

YANG Ye , YAN Xueya ,  HOU Fengzhen ,  PAN Lei

School of Science, China Pharmaceutical University, Nanjing 210009, P.R.China;

Corresponding author：

HOU Fengzhen, Email: houfz@cpu.edu.cn; PAN Lei, Email: panlei@cpu.edu.cn

Keywords：

heart rate variability; cardiovascular disease; sleep; extreme gradient boosting

DOI：

10.7507/1001-5515.202004039

Video：

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The peak period of cardiovascular disease (CVD) is around the time of awakening in the morning, which may be related to the surge of sympathetic activity at the end of nocturnal sleep. This paper chose 140 participants as study object, 70 of which had occurred CVD events while the rest hadn’t during a two-year follow-up period. A two-layer model was proposed to investigate whether hypnopompic heart rate variability (HRV) was informative to distinguish these two types of participants. In the proposed model, the extreme gradient boosting algorithm (XGBoost) was used to construct a classifier in the first layer. By evaluating the feature importance of the classifier, those features with larger importance were fed into the second layer to construct the final classifier. Three machine learning algorithms, i.e., XGBoost, random forest and support vector machine were employed and compared in the second layer to find out which one can achieve the highest performance. The results showed that, with the analysis of hypnopompic HRV, the XGBoost+XGBoost model achieved the best performance with an accuracy of 84.3%. Compared with conventional time-domain and frequency-domain features, those features derived from nonlinear dynamic analysis were more important to the model. Especially, modified permutation entropy at scale 1 and sample entropy at scale 3 were relatively important. This study might have significance for the prevention and diagnosis of CVD, as well as for the design of CVD-risk assessment system.

Citation： YANG Ye, YAN Xueya, HOU Fengzhen, PAN Lei. Study on the prediction of cardiovascular disease based on sleep heart rate variability analysis. Journal of Biomedical Engineering, 2021, 38(2): 249-256. doi: 10.7507/1001-5515.202004039 Copy

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3.	de Zambottim, Trinder J, Silvani A, et al. Dynamic coupling between the central and autonomic nervous systems during sleep: A review. Neurosci Biobehav Rev, 2018, 90: 84-103.
4.	Thayer J F, Yamamoto S S, Brosschot J F. The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. Int J Cardiol, 2010, 141(2): 122-131.
5.	Manfredini R, Boari B, Salmi R, et al. Twenty-four-hour patterns in occurrence and pathophysiology of acute cardiovascular events and ischemic heart disease. Chronobiol Int, 2013, 30(1-2): 6-16.
6.	Goff E A, Nicholas C L, Simonds A K, et al. Differential effects of waking from non-rapid eye movement versus rapid eye movement sleep on cardiovascular activity. J Sleep Res, 2010, 19(1): 201-206.
7.	Muller J E, Ludmer P L, Willich S N, et al. Circadian variation in the frequency of sudden cardiac death. Circulation, 1987, 75(1): 131-138.
8.	Willich S N, Levy D, Rocco M B, et al. Circadian variation in the incidence of sudden cardiac death in the framingham heart study population. Am J Cardiol, 1987, 60(10): 801-806.
9.	Thakur R K, Hoffmann R G, Olson D W, et al. Circadian variation in sudden cardiac death: effects of age, sex, and initial cardiac rhythm. Ann Emerg Med, 1996, 27(1): 29-34.
10.	Wang Q, Cui Y, Yogendranath P, et al. Blood pressure and heart rate variability are linked with hyperphosphatemia in chronic kidney disease patients. Chronobiol Int, 2018, 35(10): 1329-1334.
11.	Takeda N, Maemura K. Circadian clock and the onset of cardiovascular events. Hypertens Res, 2016, 39(6): 383-390.
12.	Amici A, Cicconetti P, Sagrafoli C, et al. Exaggerated morning blood pressure surge and cardiovascular events. A 5-year longitudinal study in normotensive and well-controlled hypertensive elderly. Arch Gerontol Geriat, 2009, 49(2): e105-e109.
13.	van de Borne P, Nguyen H, Biston P, et al. Effects of wake and sleep stages on the 24-h autonomic control of blood pressure and heart rate in recumbent men. Am J Physiol, 1994, 266(2): H548-H554.
14.	Furlan R, Guzzetti S, Crivellaro W, et al. Continuous 24-hour assessment of the neural regulation of systemic arterial-pressure and RR variabilities in ambulant subjects. Circulation, 1990, 81(2): 537-547.
15.	黄晓林. 心率变异性的分析方法研究. 南京: 南京大学, 2009.
16.	Pumprla J, Howorka K, Groves D, et al. Functional assessment of heart rate variability: physiological basis and practical applications. Int J Cardiol, 2002, 84(1): 1-14.
17.	Kirizawa J M, Garner D M, Arab C, et al. Is heart rate variability a valuable method to investigate cardiac autonomic dysfunction in subjects with leukemia? A systematic review to evaluate its importance in clinical practice. Support Care Cancer, 2020, 28(1): 35-42.
18.	Escorihuela R M, Capdevila L, Castro J R, et al. Reduced heart rate variability predicts fatigue severity in individuals with chronic fatigue syndrome/myalgic encephalomyelitis. J Transl Med, 2020, 18(1): 4.
19.	Endukuru C K, Gaur G S, Yerrabelli D, et al. Impaired baroreflex sensitivity and cardiac autonomic functions are associated with cardiovascular disease risk factors among patients with metabolic syndrome in a tertiary care teaching hospital of South-India. Diabetes Metab Syndr, 2020, 14(6): 2043-2051.
20.	Kubota Y, Chen L Y, Whitsel E A, et al. Heart rate variability and lifetime risk of cardiovascular disease: the atherosclerosis risk in communities study. Ann Epidemiol, 2017, 27(10): 619-625.
21.	Cardoso C L, Moraes R M, Leite N C, et al. Relationships between reduced heart rate variability and pre-clinical cardiovascular disease in patients with type 2 diabetes. Diabetes Research and Clinical Practice, 2014, 106(1): 110-117.
22.	Yan X, Zhang L, Li J, et al. Entropy-based measures of hypnopompic heart rate variability contribute to the automatic prediction of cardiovascular events. Entropy (Basel), 2020, 22(2): 241.
23.	Quan S F, Howard B V, Iber C, et al. The sleep heart health study: design, rationale, and methods. Sleep, 1997, 20(12): 1077-1085.
24.	Pan J, Tompkins W J. A real-time QRS detection algorithm. IEEE Trans Biomed Eng, 1985, 32(3): 230-236.
25.	Lippman N, Stein K M, Lerman B B. Comparison of methods for removal of ectopy in measurement of heart rate variability. Am J Physiol, 1994, 267(1): H411-H418.
26.	Camm A J, Malik M, Bigger J T, et al. Heart rate variability-standards of measurement, physiological interpretation, and clinical use. Circulation, 1996, 93(5): 1043-1065.
27.	杨萍, 侯威, 封国林. 基于去趋势波动分析方法确定极端事件阈值. 物理学报, 2008, 57(8): 5333-5342.
28.	Li R X, Wang J, Chen Y Y. Effect of the signal filtering on detrended fluctuation analysis. Physica A, 2018, 494: 446-453.
29.	Costa M, Goldberger A L, Peng C K. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett, 2002, 89(6): 068102.
30.	Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol, 2000, 278(6): H2039-H2049.
31.	Bandt C, Pompe B. Permutation entropy: a natural complexity measure for time series. Phys Rev Lett, 2002, 88(17): 4.
32.	Bian Chunhua, Qin Chang, Ma Q D, et al. Modified permutation-entropy analysis of heartbeat dynamics. Phys Rev E Stat Nonlin Soft Matter Phys, 2012, 85(2): 021906.
33.	宋小锋. 基于决策树的空气质量建模. 电子测试, 2020, 11(11): 67-68, 59.
34.	Cawley G C, Talbot N L C. On over-fitting in model selection and subsequent selection bias in performance evaluation. J Mach Learn Res, 2010, 11: 2079-2107.
35.	Luque A, Carrasco A, Martin A, et al. The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recogn, 2019, 91: 216-231.
36.	Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Inf Process Manage, 2009, 45(4): 427-437.
37.	Zhang L, Wu H L, Zhang X Y, et al. Sleep heart rate variability assists the automatic prediction of long-term cardiovascular outcomes. Sleep Med, 2020, 67: 217-224.

1. World Health Organization. Cardiovascular diseases. (2017-5-17)[2020-3-30]. https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).
2. Clark H. NCDs: a challenge to sustainable human development. Lancet, 2013, 381(9866): 510-511.
3. de Zambottim, Trinder J, Silvani A, et al. Dynamic coupling between the central and autonomic nervous systems during sleep: A review. Neurosci Biobehav Rev, 2018, 90: 84-103.
4. Thayer J F, Yamamoto S S, Brosschot J F. The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. Int J Cardiol, 2010, 141(2): 122-131.
5. Manfredini R, Boari B, Salmi R, et al. Twenty-four-hour patterns in occurrence and pathophysiology of acute cardiovascular events and ischemic heart disease. Chronobiol Int, 2013, 30(1-2): 6-16.
6. Goff E A, Nicholas C L, Simonds A K, et al. Differential effects of waking from non-rapid eye movement versus rapid eye movement sleep on cardiovascular activity. J Sleep Res, 2010, 19(1): 201-206.
7. Muller J E, Ludmer P L, Willich S N, et al. Circadian variation in the frequency of sudden cardiac death. Circulation, 1987, 75(1): 131-138.
8. Willich S N, Levy D, Rocco M B, et al. Circadian variation in the incidence of sudden cardiac death in the framingham heart study population. Am J Cardiol, 1987, 60(10): 801-806.
9. Thakur R K, Hoffmann R G, Olson D W, et al. Circadian variation in sudden cardiac death: effects of age, sex, and initial cardiac rhythm. Ann Emerg Med, 1996, 27(1): 29-34.
10. Wang Q, Cui Y, Yogendranath P, et al. Blood pressure and heart rate variability are linked with hyperphosphatemia in chronic kidney disease patients. Chronobiol Int, 2018, 35(10): 1329-1334.
11. Takeda N, Maemura K. Circadian clock and the onset of cardiovascular events. Hypertens Res, 2016, 39(6): 383-390.
12. Amici A, Cicconetti P, Sagrafoli C, et al. Exaggerated morning blood pressure surge and cardiovascular events. A 5-year longitudinal study in normotensive and well-controlled hypertensive elderly. Arch Gerontol Geriat, 2009, 49(2): e105-e109.
13. van de Borne P, Nguyen H, Biston P, et al. Effects of wake and sleep stages on the 24-h autonomic control of blood pressure and heart rate in recumbent men. Am J Physiol, 1994, 266(2): H548-H554.
14. Furlan R, Guzzetti S, Crivellaro W, et al. Continuous 24-hour assessment of the neural regulation of systemic arterial-pressure and RR variabilities in ambulant subjects. Circulation, 1990, 81(2): 537-547.
15. 黄晓林. 心率变异性的分析方法研究. 南京: 南京大学, 2009.
16. Pumprla J, Howorka K, Groves D, et al. Functional assessment of heart rate variability: physiological basis and practical applications. Int J Cardiol, 2002, 84(1): 1-14.
17. Kirizawa J M, Garner D M, Arab C, et al. Is heart rate variability a valuable method to investigate cardiac autonomic dysfunction in subjects with leukemia? A systematic review to evaluate its importance in clinical practice. Support Care Cancer, 2020, 28(1): 35-42.
18. Escorihuela R M, Capdevila L, Castro J R, et al. Reduced heart rate variability predicts fatigue severity in individuals with chronic fatigue syndrome/myalgic encephalomyelitis. J Transl Med, 2020, 18(1): 4.
19. Endukuru C K, Gaur G S, Yerrabelli D, et al. Impaired baroreflex sensitivity and cardiac autonomic functions are associated with cardiovascular disease risk factors among patients with metabolic syndrome in a tertiary care teaching hospital of South-India. Diabetes Metab Syndr, 2020, 14(6): 2043-2051.
20. Kubota Y, Chen L Y, Whitsel E A, et al. Heart rate variability and lifetime risk of cardiovascular disease: the atherosclerosis risk in communities study. Ann Epidemiol, 2017, 27(10): 619-625.
21. Cardoso C L, Moraes R M, Leite N C, et al. Relationships between reduced heart rate variability and pre-clinical cardiovascular disease in patients with type 2 diabetes. Diabetes Research and Clinical Practice, 2014, 106(1): 110-117.
22. Yan X, Zhang L, Li J, et al. Entropy-based measures of hypnopompic heart rate variability contribute to the automatic prediction of cardiovascular events. Entropy (Basel), 2020, 22(2): 241.
23. Quan S F, Howard B V, Iber C, et al. The sleep heart health study: design, rationale, and methods. Sleep, 1997, 20(12): 1077-1085.
24. Pan J, Tompkins W J. A real-time QRS detection algorithm. IEEE Trans Biomed Eng, 1985, 32(3): 230-236.
25. Lippman N, Stein K M, Lerman B B. Comparison of methods for removal of ectopy in measurement of heart rate variability. Am J Physiol, 1994, 267(1): H411-H418.
26. Camm A J, Malik M, Bigger J T, et al. Heart rate variability-standards of measurement, physiological interpretation, and clinical use. Circulation, 1996, 93(5): 1043-1065.
27. 杨萍, 侯威, 封国林. 基于去趋势波动分析方法确定极端事件阈值. 物理学报, 2008, 57(8): 5333-5342.
28. Li R X, Wang J, Chen Y Y. Effect of the signal filtering on detrended fluctuation analysis. Physica A, 2018, 494: 446-453.
29. Costa M, Goldberger A L, Peng C K. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett, 2002, 89(6): 068102.
30. Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol, 2000, 278(6): H2039-H2049.
31. Bandt C, Pompe B. Permutation entropy: a natural complexity measure for time series. Phys Rev Lett, 2002, 88(17): 4.
32. Bian Chunhua, Qin Chang, Ma Q D, et al. Modified permutation-entropy analysis of heartbeat dynamics. Phys Rev E Stat Nonlin Soft Matter Phys, 2012, 85(2): 021906.
33. 宋小锋. 基于决策树的空气质量建模. 电子测试, 2020, 11(11): 67-68, 59.
34. Cawley G C, Talbot N L C. On over-fitting in model selection and subsequent selection bias in performance evaluation. J Mach Learn Res, 2010, 11: 2079-2107.
35. Luque A, Carrasco A, Martin A, et al. The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recogn, 2019, 91: 216-231.
36. Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Inf Process Manage, 2009, 45(4): 427-437.
37. Zhang L, Wu H L, Zhang X Y, et al. Sleep heart rate variability assists the automatic prediction of long-term cardiovascular outcomes. Sleep Med, 2020, 67: 217-224.

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Study on the prediction of cardiovascular disease based on sleep heart rate variability analysis

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