Optimizing low-energy anti-fibrillation pacing: Lessons from a cellular automaton model

TL;DR

This paper uses a cellular automaton model to understand low-energy defibrillation, revealing an optimal pacing period from competing effects and proposing a feedback scheme that improves shock timing.

Contribution

It introduces a topologically motivated feedback scheme for LEAP that outperforms traditional methods and clarifies the origin of the optimal pacing period.

Findings

Optimal pacing period results from competing effects, not resonance.

A feedback scheme improves shock timing over traditional LEAP.

Model's topological features align with realistic experimental observations.

Abstract

The essential features of far-field low-energy defibrillation are elucidated using a simple cellular automaton model of excitable media. The model's topological character allows for direct correspondence with both realistic models and experiment. An optimal pacing period is shown to arise from the competition between two effects, and not a resonant response as was previously hypothesized. Finally, a topologically motivated feedback scheme is presented that outperforms traditional LEAP by identifying optimal shock timings.