Quantifying The Complex Spatiotemporal Chaos of Cardiac Fibrillation in Ionic Models Across Parameter Regimes

TL;DR

This study explores how chaos manifests in simplified cardiac models by calculating Lyapunov exponents from action potential duration data, providing methods applicable to limited clinical or experimental data.

Contribution

Introduces APD-based algorithms for quantifying spatiotemporal chaos in cardiac models, effective even with limited data and without full state variable access.

Findings

Spatial-temporal and Wolf's algorithms effectively quantify chaos.

Minimum data length and sampling density for accurate estimation identified.

APD-based methods applicable to simulation and clinical data.

Abstract

Quantifying the complexity of cardiac systems is fundamental to understanding the onset of rhythm disorders, from mild arrhythmias to life-threatening fibrillation. In this work, we investigate how chaos shows up and evolves in simplified cardiac models by calculating the Lyapunov exponent (LE) across different parameter sets. We show that both temporal and spatial LE estimators can be effectively applied to action potential duration (APD) data, even without full access to state variables. Specifically, the spatial-temporal algorithm and Wolf's algorithm are used in quantifying the complexity, with experiments demonstrating their distinct behaviors on various single-spiral patterns. We also identify the minimum data length and sampling density necessary to achieve robust and accurate estimation. Overall, our results suggest that these APD-based methods can be applied not only to simulation data but also to future clinical or experimental data, particularly when observations are limited, such as when only APD data are available for analysis.