Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid in silico and in vivo Dataset

Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid in silico and in vivo Dataset

In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. Howev...

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Bibliographic Details
Main Authors: Jorge Sánchez, Giorgio Luongo, Mark Nothstein, Laura A. Unger, Javier Saiz, Beatriz Trenor, Armin Luik, Olaf Dössel, Axel Loewe
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-07-01
Series:Frontiers in Physiology
Subjects:
atrial fibrillation
fibrosis
machine learning
bidomain
transmurality
density
Online Access:https://www.frontiersin.org/articles/10.3389/fphys.2021.699291/full

Internet

https://www.frontiersin.org/articles/10.3389/fphys.2021.699291/full

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