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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.
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.
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.
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