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The effect of circadian rhythm on the correlation and multifractality of heart rate signals during exercise

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  • Li, Jin
  • Chen, Chen
  • Yao, Qin
  • Zhang, Peng
  • Wang, Jun
  • Hu, Jing
  • Feng, Feilong

Abstract

The heart rate variability (HRV) signals can well reflect the effect of exercise on cardiac autonomic nerve function, which can be used as the theoretical index for the development of the best physical exercise program. In this paper, we used multifractal detrended fluctuation analysis (MF-DFA), mass index spectrum and multifractality spectrum to analyze the heart rate variability signals, which have been obtained in these states, such as before, during and after exercise. The purpose is to establish the relationship between the nonlinear parameter of the signals and the autonomic nerve regulation, reveal the physiological mechanism for the effect of circadian rhythm. The result shows that exercise can decrease the correlation and increase the multifractality of heart rate variability signals. In different periods, the diversity of correlation and multifractality among different signals are obviously. The result shows that the correlation of heart rate variability signals was negatively correlated with sympathetic nerve regulation and positively correlated with vagus nerve regulation; the multifractality was positively correlated with sympathetic nerve regulation and negatively correlated with vagus nerve regulation. Further studies show that the function of heart system and the balance of autonomic nerve in the afternoon are more suitable for high-intense physical exercise.

Suggested Citation

  • Li, Jin & Chen, Chen & Yao, Qin & Zhang, Peng & Wang, Jun & Hu, Jing & Feng, Feilong, 2018. "The effect of circadian rhythm on the correlation and multifractality of heart rate signals during exercise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1207-1213.
  • Handle: RePEc:eee:phsmap:v:509:y:2018:i:c:p:1207-1213
    DOI: 10.1016/j.physa.2018.06.021
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    References listed on IDEAS

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    1. Ivanov, P.Ch & Rosenblum, M.G & Peng, C.-K & Mietus, J.E & Havlin, S & Stanley, H.E & Goldberger, A.L, 1998. "Scaling and universality in heart rate variability distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 249(1), pages 587-593.
    2. B. Podobnik & D. F. Fu & H. E. Stanley & P. Ch. Ivanov, 2007. "Power-law autocorrelated stochastic processes with long-range cross-correlations," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 56(1), pages 47-52, March.
    3. B. Podobnik & I. Grosse & D. Horvatić & S. Ilic & P. Ch. Ivanov & H. E. Stanley, 2009. "Quantifying cross-correlations using local and global detrending approaches," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(2), pages 243-250, September.
    4. Plamen Ch Ivanov & Ainslie Yuen & Pandelis Perakakis, 2014. "Impact of Stock Market Structure on Intertrade Time and Price Dynamics," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-14, April.
    5. Xu, Yinlin & Ma, Qianli D.Y. & Schmitt, Daniel T. & Bernaola-Galván, Pedro & Ivanov, Plamen Ch., 2011. "Effects of coarse-graining on the scaling behavior of long-range correlated and anti-correlated signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4057-4072.
    6. Plamen Ch. Ivanov & Luís A. Nunes Amaral & Ary L. Goldberger & Shlomo Havlin & Michael G. Rosenblum & Zbigniew R. Struzik & H. Eugene Stanley, 1999. "Multifractality in human heartbeat dynamics," Nature, Nature, vol. 399(6735), pages 461-465, June.
    7. Vandewalle, N & Ausloos, M & Boveroux, Ph, 1999. "The moving averages demystified," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 269(1), pages 170-176.
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    Cited by:

    1. Jiao, Dezhao & Wang, Zikuan & Li, Jin & Feng, Feilong & Hou, Fengzhen, 2020. "The chaotic characteristics detection based on multifractal detrended fluctuation analysis of the elderly 12-lead ECG signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    2. Wang, Jian & Kim, Junseok & Shao, Wei & Nam, SeungHyon & Hong, Soon-Cheol, 2021. "Effect of oxytocin injection on fetal heart rate based on multifractal analysis," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    3. Shao, Wei & Wang, Jian, 2020. "Does the “ice-breaking” of South and North Korea affect the South Korean financial market?," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).

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