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A new dissimilarity measure based on ordinal pattern for analyzing physiological signals

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  • Liu, Yunxiao
  • Lin, Youfang
  • Jia, Ziyu
  • Wang, Jing
  • Ma, Yan

Abstract

Complex physiological signals carry critical information about the underlying dynamics of the complex system that produced them. By analyzing and exploring these signals, we can identify which state the system is in and understand the underlying dynamics of that system. Numerous linear and nonlinear methods have been developed for analyzing physiological signals, but they either rely on certain assumptions or are easily affected by various factors, which limits their applications. In this paper, we propose a novel dissimilarity measure between two signals, which is based on the ordinal pattern representation of signals. Therefore, it naturally inherits the advantages of ordinal pattern analysis, namely simplicity, robustness, and low complexity in computation without further prior assumptions. Finally, we apply this new measure to analyze several physiological signals derived from different physiological and pathological conditions to assess the effectiveness of the proposed measure. Experimental results from heart rate analysis, EEG signals analysis of healthy subjects and patients with epilepsy, and analysis of EEG signals at different sleep stages in healthy subjects demonstrate that the new measure is capable of distinguishing signals from different conditions and is therefore a useful tool for analyzing various physiological signals.

Suggested Citation

  • Liu, Yunxiao & Lin, Youfang & Jia, Ziyu & Wang, Jing & Ma, Yan, 2021. "A new dissimilarity measure based on ordinal pattern for analyzing physiological signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    • Handle: RePEc:eee:phsmap:v:574:y:2021:i:c:s0378437121002697
    DOI: 10.1016/j.physa.2021.125997
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    References listed on IDEAS

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    3. Keller, K. & Sinn, M., 2005. "Ordinal analysis of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 356(1), pages 114-120.
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