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Multiscale ordinal network analysis of human cardiac dynamics.

M McCullough1, M Small2,3,4, H H C Iu3,5

  • 1School of Mathematics and Statistics, The University of Western Australia, Crawley, Western Australia 6009, Australia michael.mccullough@research.uwa.edu.au.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|May 17, 2017
PubMed
Summary

We developed a new information theory measure to quantify biological system complexity from time-series data. This method distinguishes cardiac rhythms and analyzes age-related complexity in heart dynamics.

Keywords:
complex networksnetwork entropynonlinear time-series analysisordinal patternssymbolic dynamics

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Area of Science:

  • Complex systems biology
  • Information theory
  • Biomedical signal processing

Background:

  • Quantifying biological system complexity is crucial for understanding health and disease.
  • Time-series data, such as electrocardiograms (ECG) and interbeat intervals (IBI), are rich sources of information about physiological dynamics.
  • Existing methods for complexity analysis may not fully capture the multiscale nature of biological systems.

Purpose of the Study:

  • To introduce a novel information-theoretic measure for quantifying biological system complexity using time-series data.
  • To demonstrate the application of this measure in analyzing human cardiac dynamics.
  • To investigate the multiscale complexity of cardiac function in relation to aging.

Main Methods:

  • Extension of the symbolic mapping procedure for permutation entropy.
  • Construction of an ordinal network model based on time-series order patterns.
  • Computation of an entropic measure of transitional complexity from the ordinal network.
  • Analysis of electrocardiogram (ECG) and interbeat interval (IBI) time series.

Main Results:

  • The proposed method effectively discriminates between normal sinus rhythm, ventricular tachycardia, and ventricular fibrillation in ECG data.
  • Multiscale complexity analysis of IBI time series reveals age-related changes in cardiac dynamics.
  • Findings align with previous studies linking age to fractal-like long-range correlations in cardiac function.

Conclusions:

  • The developed information-theoretic measure provides a robust tool for quantifying biological system complexity.
  • The ordinal network approach offers insights into the transitional dynamics and multiscale properties of physiological systems.
  • This method has significant potential for clinical applications in cardiology and understanding aging processes.