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Multiscale permutation entropy analysis of electrocardiogram

Author

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  • Liu, Tiebing
  • Yao, Wenpo
  • Wu, Min
  • Shi, Zhaorong
  • Wang, Jun
  • Ning, Xinbao

Abstract

To make a comprehensive nonlinear analysis to ECG, multiscale permutation entropy (MPE) was applied to ECG characteristics extraction to make a comprehensive nonlinear analysis of ECG. Three kinds of ECG from PhysioNet database, congestive heart failure (CHF) patients, healthy young and elderly subjects, are applied in this paper. We set embedding dimension to 4 and adjust scale factor from 2 to 100 with a step size of 2, and compare MPE with multiscale entropy (MSE). As increase of scale factor, MPE complexity of the three ECG signals are showing first-decrease and last-increase trends. When scale factor is between 10 and 32, complexities of the three ECG had biggest difference, entropy of the elderly is 0.146 less than the CHF patients and 0.025 larger than the healthy young in average, in line with normal physiological characteristics. Test results showed that MPE can effectively apply in ECG nonlinear analysis, and can effectively distinguish different ECG signals.

Suggested Citation

  • Liu, Tiebing & Yao, Wenpo & Wu, Min & Shi, Zhaorong & Wang, Jun & Ning, Xinbao, 2017. "Multiscale permutation entropy analysis of electrocardiogram," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 492-498.
    • Handle: RePEc:eee:phsmap:v:471:y:2017:i:c:p:492-498
    DOI: 10.1016/j.physa.2016.11.102
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    Cited by:

    1. Liu, Hongzhi & Zhang, Xie & Hu, Huaqing & Zhang, Xingchen, 2022. "Exploring the impact of flow values on multiscale complexity quantification of airport flight flow fluctuations," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    2. Mukherjee, Sayan & Banerjee, Santo & Rondoni, Lamberto, 2018. "Dispersive graded entropy on computing dynamical complexity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 131-140.
    3. Chen, Yu & Ling, Guang & Song, Xiangxiang & Tu, Wenhui, 2023. "Characterizing the statistical complexity of nonlinear time series via ordinal pattern transition networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    4. Yan, Bo & Palit, Sanjay K. & Mukherjee, Sayan & Banerjee, Santo, 2019. "Signature of complexity in time–frequency domain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).

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