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Human photoplethysmogram: new insight into chaotic characteristics

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  • Sviridova, Nina
  • Sakai, Kenshi

Abstract

The photoplethysmogram is widely used in medical settings and sports equipment to measure biological signals. The photoplethysmogram, which is measured noninvasively, can provide valuable information about cardiovascular system performance. The present study sought to investigate the underlying dynamics of photoplethysmographic signals from healthy young human subjects. In previous studies the photoplethysmogram was claimed to be driven by deterministic chaos [Tsuda 1992, Sumida 2000]; however, the methods applied for chaos detection were noise sensitive and inconclusive. Therefore, to reach a consistent conclusion it is important to employ additional nonlinear time series analysis tools that can test different features of the signal's underlying dynamics. In this paper, methods of nonlinear time series analysis, including time delay embedding, largest Lyapunov exponent, deterministic nonlinear prediction, Poincaré section, the Wayland test and method of surrogate data were applied to photoplethysmogram time series to identify the unique characteristics of the photoplethysmogram as a dynamical system. Results demonstrated that photoplethysmogram dynamics is consistent with the definition of chaotic movement, and its chaotic properties showed some similarity to Rossler's single band chaos with induced dynamical noise. Additionally it was found that deterministic nonlinear prediction, Poincaré section and the Wayland test can reveal important characteristics of photoplethysmographic signals that will be important tools for theoretical and applied studies on the photoplethysmogram.

Suggested Citation

  • Sviridova, Nina & Sakai, Kenshi, 2015. "Human photoplethysmogram: new insight into chaotic characteristics," Chaos, Solitons & Fractals, Elsevier, vol. 77(C), pages 53-63.
    • Handle: RePEc:eee:chsofr:v:77:y:2015:i:c:p:53-63
    DOI: 10.1016/j.chaos.2015.05.005
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    References listed on IDEAS

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    1. McClintock, Peter V.E. & Stefanovska, Aneta, 2002. "Noise and determinism in cardiovascular dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 314(1), pages 69-76.
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    3. Pham, Tuan D. & Thang, Truong Cong & Oyama-Higa, Mayumi & Sugiyama, Masahide, 2013. "Mental-disorder detection using chaos and nonlinear dynamical analysis of photoplethysmographic signals," Chaos, Solitons & Fractals, Elsevier, vol. 51(C), pages 64-74.
    4. 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.
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    Cited by:

    1. Lahmiri, Salim, 2017. "On fractality and chaos in Moroccan family business stock returns and volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 29-39.
    2. Sviridova, Nina & Zhao, Tiejun & Aihara, Kazuyuki & Nakamura, Kazuyuki & Nakano, Akimasa, 2018. "Photoplethysmogram at green light: Where does chaos arise from?," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 157-165.
    3. Adil Yilmaz & Gazanfer Unal, 2016. "Chaos in Fractionally Integrated Generalized Autoregressive Conditional Heteroskedastic Processes," Papers 1601.08099, arXiv.org, revised Feb 2016.
    4. Lahmiri, Salim, 2017. "Investigating existence of chaos in short and long term dynamics of Moroccan exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 655-661.
    5. Lahmiri, Salim, 2017. "A study on chaos in crude oil markets before and after 2008 international financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 389-395.
    6. Goshvarpour, Atefeh & Goshvarpour, Ateke, 2018. "Poincaré's section analysis for PPG-based automatic emotion recognition," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 400-407.

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