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

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.

SUBMITTER: McGillivray MF 

PROVIDER: S-EPMC5936952 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature