Modeling the Heart as a Communication System

TL;DR

This paper introduces a novel theoretical framework using information theory to quantify electrical communication in cardiac tissue, revealing how arrhythmias disrupt cell-to-cell information sharing and potentially guiding targeted therapies.

Contribution

It develops a new information theory-based approach to analyze electrical communication in cardiac arrhythmias, providing spatial and rhythm-specific insights beyond traditional metrics.

Findings

Information sharing is spatially heterogeneous during arrhythmias.

Arrhythmias significantly alter entropy and mutual information in cardiac tissue.

Entropy mapping can identify reentry cores for targeted ablation.

Abstract

Electrical communication between cardiomyocytes can be perturbed during arrhythmia, but these perturbations are not captured by conventional electrocardiographic metrics. We developed a theoretical framework to quantify electrical communication using information theory metrics in 2-dimensional cell lattice models of cardiac excitation propagation. The time series generated by each cell was coarse-grained to 1 when excited or 0 when resting. The Shannon entropy for each cell was calculated from the time series during four clinically important heart rhythms: normal heartbeat, anatomical reentry, spiral reentry, and multiple reentry. We also used mutual information to perform spatial profiling of communication during these cardiac arrhythmias. We found that information sharing between cells was spatially heterogeneous. In addition, cardiac arrhythmia significantly impacted information sharing within the heart. Entropy localized the path of the drifting core of spiral reentry, which could be an optimal target of therapeutic ablation. We conclude that information theory metrics can quantitatively assess electrical communication among cardiomyocytes. The traditional concept of the heart as a functional syncytium sharing electrical information cannot predict altered entropy and information sharing during complex arrhythmia. Information theory metrics may find clinical application in the identification of rhythm-specific treatments which are currently unmet by traditional electrocardiographic techniques.