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Signature of complexity in time–frequency domain

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Listed:
  • Yan, Bo
  • Palit, Sanjay K.
  • Mukherjee, Sayan
  • Banerjee, Santo

Abstract

We propose a time–frequency based complexity to measure disorder in the long term dynamics of a signal. The disorder is characterized by defining an wavelet spectrogram space in a multiscale coordinate system. The multiscale coordinate system is formed with Wavelet coefficient of the signal. Further, an Weighted entropy measure is implemented to quantify the aforesaid disorder. Numerical results support the proposed method. The proposed entropy is successfully applied to distinguish the ECG signals of normal healthy person and congestive heart failure patients.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:535:y:2019:i:c:s0378437119314001
    DOI: 10.1016/j.physa.2019.122433
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    References listed on IDEAS

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    1. 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.
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    6. He, Shaobo & Banerjee, Santo, 2018. "Epidemic outbreaks and its control using a fractional order model with seasonality and stochastic infection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 408-417.
    7. He, Shaobo & Banerjee, Santo, 2018. "Multicavity formations and complexity modulation in a hyperchaotic discrete system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 366-377.
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