Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation

Royal Society Open Science
Max Falkenberg McGillivrayKim Christensen

Abstract

Mapping resolution has recently been identified as a key limitation in successfully locating the drivers of atrial fibrillation (AF). Using a simple cellular automata model of AF, we demonstrate a method by which re-entrant drivers can be located quickly and accurately using a collection of indirect electrogram measurements. The method proposed employs simple, out-of-the-box machine learning algorithms to correlate characteristic electrogram gradients with the displacement of an electrogram recording from a re-entrant driver. Such a method is less sensitive to local fluctuations in electrical activity. As a result, the method successfully locates 95.4% of drivers in tissues containing a single driver, and 95.1% (92.6%) for the first (second) driver in tissues containing two drivers of AF. Additionally, we demonstrate how the technique can be applied to tissues with an arbitrary number of drivers. In its current form, the techniques presented are not refined enough for a clinical setting. However, the methods proposed offer a promising path for future investigations aimed at improving targeted ablation for AF.

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Citations

Jul 2, 2020·Physical Review Research·Alberto CiacciKim Christensen
Jan 23, 2020·Physical Review. E·Max FalkenbergKim Christensen
Sep 15, 2020·Circulation. Arrhythmia and Electrophysiology·Alexander M ZolotarevVadim V Fedorov
Feb 19, 2021·Circulation Research·Natalia A TrayanovaJulie K Shade
Aug 16, 2021·European Heart Journal·Venkat D NagarajanSabine Ernst
Sep 16, 2020·Computers in Biology and Medicine·Victor Gonçalves MarquesJoão Salinet

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