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Multiscale based nonlinear dynamics analysis of heart rate variability signals

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  • Syed Zaki Hassan Kazmi
  • Nazneen Habib
  • Rabia Riaz
  • Sanam Shahla Rizvi
  • Syed Ali Abbas
  • Tae-Sun Chung

Abstract

Acceleration change index (ACI) is a fast and easy to understand heart rate variability (HRV) analysis approach used for assessing cardiac autonomic control of the nervous systems. The cardiac autonomic control of the nervous system is an example of highly integrated systems operating at multiple time scales. Traditional single scale based ACI did not take into account multiple time scales and has limited capability to classify normal and pathological subjects. In this study, a novel approach multiscale ACI (MACI) is proposed by incorporating multiple time scales for improving the classification ability of ACI. We evaluated the performance of MACI for classifying, normal sinus rhythm (NSR), congestive heart failure (CHF) and atrial fibrillation subjects. The findings reveal that MACI provided better classification between healthy and pathological subjects compared to ACI. We also compared MACI with other scale-based techniques such as multiscale entropy, multiscale permutation entropy (MPE), multiscale normalized corrected Shannon entropy (MNCSE) and multiscale permutation entropy (IMPE). The preliminary results show that MACI values are more stable and reliable than IMPE and MNCSE. The results show that MACI based features lead to higher classification accuracy.

Suggested Citation

  • Syed Zaki Hassan Kazmi & Nazneen Habib & Rabia Riaz & Sanam Shahla Rizvi & Syed Ali Abbas & Tae-Sun Chung, 2020. "Multiscale based nonlinear dynamics analysis of heart rate variability signals," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-14, December.
    • Handle: RePEc:plo:pone00:0243441
    DOI: 10.1371/journal.pone.0243441
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    References listed on IDEAS

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    1. Syed Zaki Hassan Kazmi & Henggui Zhang & Wajid Aziz & Oliver Monfredi & Syed Ali Abbas & Saeed Arif Shah & Syeda Sobia Hassan Kazmi & Wasi Haider Butt, 2016. "Inverse Correlation between Heart Rate Variability and Heart Rate Demonstrated by Linear and Nonlinear Analysis," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-10, June.
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