Research of short-term heart rate variability during sleep based on limited penetrable horizontal visibility graph

Huo Cheng-Yu; Ma Xiao-Fei; Ning Xin-Bao

doi:10.7498/aps.66.160502

Abstract

Heart rate is one of the most easily accessed human physiological data. In recent years, the analysis of sleep function based on heart rate variability has become a new popular feature of wearable devices used for daily health management. Consequently, it is needed to explore various types of short-term characteristic parameters which can be applied to the heartbeat interval time series within the standard sleep staging time window (about 30 s). Utilizing the recently reported limited penetrable horizontal visibility graph (LPHVG) algorithm, together with a weighted limited penetrable horizontal visibility graph (WLPHVG) algorithm proposed in this paper, the short-term heartbeat interval time series in different sleep stages are mapped to networks respectively. Then, 6 characteristic parameters, including the average clustering coefficient C, the characteristic path length L, the clustering coefficient entropy Ec, the distance distribution entropy Ed, the weighted clustering coefficient entropy ECw and the weight distribution entropy Ew are calculated and analyzed. The results show that the values of these characteristic parameters are significantly different in the states of wakefulness, light sleep, deep sleep and rapid eye movement, especially in the case of the limited penetrable distance Lp=1, indicating the effectiveness of LPHVG and WLPHVG algorithm in sleep staging based on short-term heartbeat interval time series. In addition, a preliminary comparison between proposed algorithm and the basic visibility graph (VG) algorithm shows that in this case, the LPHVG and WLPHVG algorithm are superior to the basic VG algorithm both in performance and in calculation speed. Meanwhile, based on the LPHVG and WLPHVG algorithm, the values of network parameters (the clustering coefficient entropy Ec and the weighted clustering coefficient entropy ECw) are calculated from heartbeat interval time series of healthy young and elder subjects in different sleep stages, to further study the aging effect on and sleep regulation over cardiac dynamics. It is found that despite an overall level difference between the values of Ec and ECw in young and elder groups, the stratification patterns across different sleep stages almost do not break down with advanced age, suggesting that the effect of sleep regulation on cardiac dynamics is significantly stronger than the effect of healthy aging. In addition, compared with the clustering coefficient entropy Ec based on LPHVG algorithm, the weighted clustering coefficient entropy ECw based on WLPHVG algorithm shows higher sensitivity to discriminating subtle differences in cardiac dynamics among different sleep states. Overall, it is shown that with the simple mapping criteria and low computational complexity, the proposed method could be used as a new auxiliary tool for sleep studies based on heart rate variability, and the corresponding network parameters could be used in wearable device as new auxiliary parameters for sleep staging.

Keywords:

limited penetrable horizontal visibility graph /
sleep /
heart rate variability

Authors and contacts

1.
School of Physics and Electronic Engineering, Changshu Institute of Technology, Changshu 215500, China;
2.
Institute of Biomedical Electronic Engineering, School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China

Corresponding author: Huo Cheng-Yu, hcy@cslg.edu.cn

Funds: Project supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 15KJD310002), the National Natural Science Foundation of China (Grant No. 61402057), and Jiangsu Overseas Research Training Program for University Prominent Young Middle-aged Teachers and Presidents (2016).

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Cited By

[1]	Adnane M, Jiang Z, Yan Z 2012 Expert Syst. Appl. 39 1401
[2]	Iber C, Ancoli-Israel S, Chesson A, Quan S F 2007 The AASM Manual for the Scoring of Sleep and Associated Events:Rules, Terminology and Technical Specifications (Westchester, IL:American Academy of Sleep Medicine) pp16-30
[3]	Long X, Fonseca P, Aarts R M, Haakma R, Foussier J 2014 Appl. Phys. Lett. 105 203701
[4]	Long X, Arends J B, Aarts R M, Haakma R, Fonseca P, Rolink J 2015 Appl. Phys. Lett. 106 143702
[5]	Rechtschaffen A, Kales A 1968 A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects (Washington DC:Public Health Service, US Government Printing Office) pp1-57
[6]	Ma Q L, Bian C H, Wang J 2010 Acta Phys. Sin. 59 4480 (in Chinese)[马千里, 卞春华, 王俊2010 59 4480]
[7]	Stein P K, Pu Y 2012 Sleep Med. Rev. 16 47
[8]	Bonnet M H, Arand D L 1997 Electroencephalogr. Clin. Neurophysiol. 102 390
[9]	Huo C, Huang X, Zhuang J, Hou F, Ni H, Ning X 2013 Physica A 392 3601
[10]	Liu D Z, Wang J, Li J, Li Y, Xu W M, Zhao X 2014 Acta Phys. Sin. 63 198703 (in Chinese)[刘大钊, 王俊, 李锦, 李瑜, 徐文敏, 赵筱2014 63 198703]
[11]	Ebrahimi F, Setarehdan S K, Ayala-Moyeda J, Nazeran H 2013 Comput. Methods Programs Biomed. 112 47
[12]	Xiao M, Yan H, Song J, Yang Y, Yang X 2013 Biomed. Signal Process. Control 8 624
[13]	Vigo D E, Dominguez J, Guinjoan S M, Scaramal M, Ruffa E, Solerno J, Siri L N, Cardinali D P 2010 Auton. Neurosci. 154 84
[14]	Huang R, Lai C, Lee S, Wang W, Tseng L, Chen Y, Chang S, Chung A, Ting H 2016 Sleep Breath. 20 975
[15]	Smith A L, Owen H, Reynolds K J 2013 J. Clin. Monit. Comput. 27 569
[16]	Bashan A, Bartsch R P, Kantelhardt J W, Havlin S, Ivanov P C 2012 Nat. Commun. 3 702
[17]	Hou F Z, Dai J F, Liu X F, Huang X L 2013 Appl. Phys. Lett. 102 253702
[18]	Bartsch R P, Liu K K L, Bashan A, Ivanov P C 2015 PLOS One 10 e0142143
[19]	Liu K K L, Bartsch R P, Lin A, Mantegna R N, Ivanov P C 2015 Front. Neural Circuits 9 62
[20]	Jiang S, Bian C, Ning X, Ma Q D 2013 Appl. Phys. Lett. 102 253702
[21]	Hou F, Wang J, Wu X, Yan F 2014 Europhys. Lett. 107 58001
[22]	Wang M, Tian L 2016 Physica A 461 456
[23]	Xiao Q, Pan X, Li X L, Mutua S, Yang H J, Jiang Y, Wang J Y, Zhang Q J 2014 Chin. Phys. B 23 078904
[24]	Lacasa L, Luque B, Ballesteros F, Luque J, Nuno J C 2008 Proc. Natl. Acad. Sci. USA 105 4972
[25]	Liu Z, Sun J, Zhang Y, Rolfe P 2016 Biomed. Signal Process. Control 30 86
[26]	Hou F Z, Li F W, Wang J, Yan F R 2016 Physica A 458 140
[27]	Luque B, Lacasa L, Ballesteros F, Luque J 2009 Phys. Rev. E 80 046103
[28]	Gonalves B A, Carpi L, Rosso O A, Ravetti M G 2016 Physica A 464 93
[29]	Gao Z, Cai Q, Yang Y, Dang W, Zhang S 2016 Sci. Rep. 6 35622
[30]	Zhou T T, Jin N D, Gao Z K, Luo Y B 2012 Acta Phys. Sin. 61 030506 (in Chinese)[周婷婷, 金宁德, 高忠科, 罗跃斌2012 61 030506]
[31]	Gao Z K, Hu L D, Zhou T T, Jin N D 2013 Acta Phys. Sin. 62 110507 (in Chinese)[高忠科, 胡沥丹, 周婷婷, 金宁德2013 62 110507]
[32]	Watts D J, Strogatz S H 1998 Nature 393 440
[33]	Gao Z, Yang Y, Fang P, Zou Y, Xia C, Du M 2015 Europhys. Lett. 109 30005
[34]	Zhu G, Li Y, Wen P 2014 Comput. Meth. Prog. Bio. 115 64
[35]	Gao Z, Fang P, Ding M, Jin N 2015 Exp. Therm. Fluid Sci. 60 157
[36]	Varoneckas G, Martinkenas A, Podlipskyte A, Varoneckas A, Zilinskas A 2006 Proceedings of Med-e-Tel 2006 Luxembourg, G. D. of Luxembourg, April 5-7, 2006 p371
[37]	Tobaldini E, Nobili L, Strada S, Casali K R, Braghiroli A, Montano N 2013 Front. Physiol. 4 294
[38]	Trinder J, Kleiman J, Carrington M, Smith S, Breen S, Tan N, Kim Y 2001 J. Sleep Res. 10 253
[39]	Baharav A, Kotagal S, Gibbons V, Rubin B K, Pratt G, Karin J, Akselrod S 1995 Neurology 45 1183
[40]	Versace F, Mozzato M, de Min Tona G, Cavallero C, Stegagno L 2003 Biol. Psychol. 63 149
[41]	Schmitt D T, Stein P K, Ivanov P C 2009 IEEE Trans. Biomed. Eng. 56 1564
[42]	Crasset V, Mezzetti S, Antoine M, Linkowski P, Degaute J P, van de Borne P 2001 Circulation 103 84

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Vol. 66, Issue 16 - 2017-08-20

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