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Multiscale Cumulative Residual Dispersion Entropy with Applications to Cardiovascular Signals.

Youngjun Kim1, Young-Seok Choi1

  • 1Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, Republic of Korea.

Entropy (Basel, Switzerland)
|November 24, 2023
PubMed
Summary
This summary is machine-generated.

A new method, multiscale cumulative residual dispersion entropy (MCRDE), enhances heart rate variability analysis for detecting heart conditions like congestive heart failure and atrial fibrillation.

Keywords:
R-R intervalcomplexitycumulative residual dispersion entropyelectrocardiogramheart rate variability

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Area of Science:

  • Physiology
  • Biomedical Engineering
  • Data Science

Background:

  • Heart rate variability (HRV) reflects autonomic nervous system function and aids in detecting heart diseases.
  • Entropy analysis is valuable for nonlinear time series like HRV, with dispersion entropy (DE) offering computational efficiency.
  • Existing DE methods do not account for pattern order in probability distributions.

Purpose of the Study:

  • Introduce multiscale cumulative residual dispersion entropy (MCRDE) for improved time series complexity estimation.
  • Address the limitation of DE by incorporating pattern order and multiscale analysis.
  • Evaluate MCRDE's efficacy in distinguishing physiological conditions using HRV data.

Main Methods:

  • Developed MCRDE by combining cumulative residual entropy and DE across multiple temporal scales.
  • Applied MCRDE to inter-beat interval (IBI) data from electrocardiogram (ECG) signals.
  • Compared MCRDE performance against established multiscale entropy methods using statistical tests.

Main Results:

  • MCRDE provides a generalized and fast estimation of complexity in temporal structures.
  • MCRDE demonstrated superior capability in quantifying physiological conditions compared to existing multiscale entropy methods.
  • Analysis of ECG signals from congestive heart failure (CHF), atrial fibrillation (AF), and healthy groups showed MCRDE yielded more statistically significant differences (lower p-values).

Conclusions:

  • MCRDE offers a more sensitive and robust approach to analyzing HRV complexity.
  • The proposed method enhances the detection and differentiation of cardiac conditions through improved complexity quantification.
  • MCRDE shows significant potential for clinical application in cardiovascular disease monitoring.