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Updated: May 25, 2026
09:09
In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Symbolic coupling traces for causality analysis of cardiovascular control
N Wessel1, A Suhrbier, M Riedl
1Department of Physics, Humboldt-Universität zu Berlin, Germany. wessel@physik.hu-berlin.de
Summary
This study introduces a new method using symbolic dynamics to detect time-delayed coupling in biosignals. This approach offers improved quantification of cardiovascular regulation, especially in noisy, nonstationary data.
Area of Science:
- Biomedical Engineering
- Time Series Analysis
- Cardiovascular Physiology
Background:
- Directional coupling analysis of time series is crucial for understanding complex biological systems.
- Existing methods like mutual information and cross recurrence analysis have limitations, particularly with nonstationary and noisy data.
Purpose of the Study:
- To present a novel method based on symbolic dynamics for detecting time-delayed coupling in biosignals.
- To evaluate the efficacy of this new method against established techniques using model systems and real-world cardiological data.
- To analyze cardiovascular regulation during different sleep stages and identify potential pathological changes.
Main Methods:
- Development of symbolic coupling traces derived from bivariate word distribution.
- Comparison of symbolic coupling traces with mutual information and cross recurrence analysis.
- Application to model systems, cardiological data, and cardiovascular measurements during sleep stages.
Main Results:
- Symbolic coupling traces provide more reliable quantification of coupling, outperforming established methods in nonstationary and noisy conditions.
- Significant differences in cardiovascular regulatory mechanisms were identified between deep sleep and other sleep stages.
- Distinctions in regulatory mechanisms were also observed between healthy subjects and patients.
Conclusions:
- The symbolic coupling traces method is effective for analyzing time-delayed coupling in biosignals, particularly in challenging data.
- This method can help detect pathological changes in cardiovascular regulation.
- It also aids in understanding the effects of therapies like continuous positive airway pressure on the cardiovascular system.
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