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Quadrantal multi-scale distribution entropy analysis of heartbeat interval series based on a modified Poincaré plot

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  • Huo, Chengyu
  • Huang, Xiaolin
  • Zhuang, Jianjun
  • Hou, Fengzhen
  • Ni, Huangjing
  • Ning, Xinbao

Abstract

The Poincaré plot is one of the most important approaches in human cardiac rhythm analysis. However, further investigations are still needed to concentrate on techniques that can characterize the dispersion of the points displayed by a Poincaré plot. Based on a modified Poincaré plot, we provide a novel measurement named distribution entropy (DE) and propose a quadrantal multi-scale distribution entropy analysis (QMDE) for the quantitative descriptions of the scatter distribution patterns in various regions and temporal scales. We apply this method to the heartbeat interval series derived from healthy subjects and congestive heart failure (CHF) sufferers, respectively, and find that the discriminations between them are most significant in the first quadrant, which implies significant impacts on vagal regulation brought about by CHF. We also investigate the day–night differences of young healthy people, and it is shown that the results present a clearly circadian rhythm, especially in the first quadrant. In addition, the multi-scale analysis indicates that the results of healthy subjects and CHF sufferers fluctuate in different trends with variation of the scale factor. The same phenomenon also appears in circadian rhythm investigations of young healthy subjects, which implies that the cardiac dynamic system is affected differently in various temporal scales by physiological or pathological factors.

Suggested Citation

  • Huo, Chengyu & Huang, Xiaolin & Zhuang, Jianjun & Hou, Fengzhen & Ni, Huangjing & Ning, Xinbao, 2013. "Quadrantal multi-scale distribution entropy analysis of heartbeat interval series based on a modified Poincaré plot," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3601-3609.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:17:p:3601-3609
    DOI: 10.1016/j.physa.2013.03.053
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    References listed on IDEAS

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    1. Hou, Fengzhen & Zhuang, Jianjun & Bian, Chunhua & Tong, Tangji & Chen, Ying & Yin, Jie & Qiu, Xiaojun & Ning, Xinbao, 2010. "Analysis of heartbeat asymmetry based on multi-scale time irreversibility test," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 754-760.
    2. Ivanov, Plamen Ch. & Chen, Zhi & Hu, Kun & Eugene Stanley, H., 2004. "Multiscale aspects of cardiac control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(3), pages 685-704.
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    Cited by:

    1. Elena Ioana Iconaru & Manuela Mihaela Ciucurel & Luminita Georgescu & Mariana Tudor & Constantin Ciucurel, 2021. "The Applicability of the Poincaré Plot in the Analysis of Variability of Reaction Time during Serial Testing," IJERPH, MDPI, vol. 18(7), pages 1-13, April.
    2. Han, Yun-Feng & Ren, Ying-Yu & He, Yuan-Sheng & Jin, Ning-De, 2018. "Variability analysis of droplet distribution of oil-in-water emulsions with a multi-scale first-order difference conductance series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 196-210.
    3. Zhai, Lu-Sheng & Zong, Yan-Bo & Wang, Hong-Mei & Yan, Cong & Gao, Zhong-Ke & Jin, Ning-De, 2017. "Characterization of flow pattern transitions for horizontal liquid–liquid pipe flows by using multi-scale distribution entropy in coupled 3D phase space," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 136-147.
    4. Zhai, Lusheng & Wu, Yinglin & Yang, Jie & Xie, Hailin, 2020. "Characterizing initiation of gas–liquid churn flows using coupling analysis of multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    5. Lin, Aijing & Shang, Pengjian, 2016. "Multifractality of stock markets based on cumulative distribution function and multiscale multifractal analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 527-534.

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