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Phase relationships between two or more interacting processes from one-dimensional time series. II. Application to
N B Janson1, A G Balanov, V S Anishchenko
1Department of Physics, Lancaster University, Lancaster, LA1 4YB, United Kingdom.
Summary
This study applied a new method to analyze heart rate variability (HRV) data, detecting synchronization between cardiovascular processes. Findings reveal varying interaction strengths and model applicability in healthy individuals.
Area of Science:
- Cardiovascular Physiology
- Nonlinear Dynamics
- Biomedical Signal Processing
Background:
- Heart Rate Variability (HRV) analysis is crucial for understanding cardiovascular regulation.
- Detecting synchronization in physiological systems can reveal underlying dynamics.
- Previous methods for synchronization detection from univariate data have limitations.
Purpose of the Study:
- To apply a novel synchronization detection approach to human heart rate variability (HRV) data.
- To investigate the behavior of angles in return times maps derived from HRV.
- To assess the model's ability to describe interactions within the cardiovascular system.
Main Methods:
- Application of a recently proposed synchronization detection method.
- Analysis of angles in return times maps from univariate heart rate variability (HRV) data.
- Modeling of interactions between cardiovascular system processes.
Main Results:
- Weak interactions between cardiovascular processes were observed in many subjects, fitting the derived model.
- Distinctive angle map structures, not captured by the model, appeared in some subjects due to strong interactions.
- Synchronization between involved processes was frequently detected.
- Instantaneous radii in the analysis were found to be disordered.
Conclusions:
- The new method effectively detects synchronization in human HRV data.
- Cardiovascular system interactions vary in strength, influencing model fit.
- Synchronization is a common phenomenon in the cardiovascular system, detectable through HRV analysis.