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

Noah DeTal; Flavio Fenton

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arXiv · 2109.10861

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

Noah DeTal·Flavio Fenton

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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.

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Noah DeTal, Flavio Fenton. 2021-09-22. Optimizing low-energy anti-fibrillation pacing: Lessons from a cellular automaton model. https://arxiv.org/abs/2109.10861

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