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Can complexity decrease in congestive heart failure?

Author

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  • Mukherjee, Sayan
  • Palit, Sanjay Kumar
  • Banerjee, Santo
  • Ariffin, M.R.K.
  • Rondoni, Lamberto
  • Bhattacharya, D.K.

Abstract

The complexity of a signal can be measured by the Recurrence period density entropy (RPDE) from the reconstructed phase space. We have chosen a window based RPDE method for the classification of signals, as RPDE is an average entropic measure of the whole phase space. We have observed the changes in the complexity in cardiac signals of normal healthy person (NHP) and congestive heart failure patients (CHFP). The results show that the cardiac dynamics of a healthy subject is more complex and random compare to the same for a heart failure patient, whose dynamics is more deterministic. We have constructed a general threshold to distinguish the border line between a healthy and a congestive heart failure dynamics. The results may be useful for wide range for physiological and biomedical analysis.

Suggested Citation

  • Mukherjee, Sayan & Palit, Sanjay Kumar & Banerjee, Santo & Ariffin, M.R.K. & Rondoni, Lamberto & Bhattacharya, D.K., 2015. "Can complexity decrease in congestive heart failure?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 439(C), pages 93-102.
    • Handle: RePEc:eee:phsmap:v:439:y:2015:i:c:p:93-102
    DOI: 10.1016/j.physa.2015.07.030
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    Citations

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    Cited by:

    1. 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).
    2. Ahmed, Umair & Carpitella, Silvia & Certa, Antonella, 2021. "An integrated methodological approach for optimising complex systems subjected to predictive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Gros, Daniel & De Groen, Willem Pieter, 2015. "Will the Single Resolution Fund be a �baby tiger� during the transition?," CEPS Papers 11192, Centre for European Policy Studies.
    4. Das, Parthasakha & Das, Pritha & Mukherjee, Sayan, 2020. "Stochastic dynamics of Michaelis–Menten kinetics based tumor-immune interactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    5. Леиашвили, Паата, 2022. "Экономика Как Сложная Система: В Поисках Новой Парадигмы [The Economy as a Nonlinear Complex System: In Search of a New Paradigm]," MPRA Paper 114140, University Library of Munich, Germany.
    6. Natiq, Hayder & Banerjee, Santo & He, Shaobo & Said, M.R.M. & Kilicman, Adem, 2018. "Designing an M-dimensional nonlinear model for producing hyperchaos," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 506-515.
    7. 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.
    8. He, Shaobo & Sun, Kehui & Wang, Huihai, 2016. "Multivariate permutation entropy and its application for complexity analysis of chaotic systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 812-823.
    9. Köberle, Alexandre C. & Garaffa, Rafael & Cunha, Bruno S.L. & Rochedo, Pedro & Lucena, André F.P. & Szklo, Alexandre & Schaeffer, Roberto, 2018. "Are conventional energy megaprojects competitive? Suboptimal decisions related to cost overruns in Brazil," Energy Policy, Elsevier, vol. 122(C), pages 689-700.
    10. Natiq, Hayder & Banerjee, Santo & Misra, A.P. & Said, M.R.M., 2019. "Degenerating the butterfly attractor in a plasma perturbation model using nonlinear controllers," Chaos, Solitons & Fractals, Elsevier, vol. 122(C), pages 58-68.

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