Atrial fibrillation signatures on intracardiac electrograms identified by deep learning.

ABSTRACT:

Background

Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy.

Objective

We hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) signatures.

Methods

We studied 86 patients in whom the diagnosis of AF or AT was established at electrophysiological study (25 female, 65 ± 11 years). Custom DL architectures were trained to identify AF using N = 29,340 unipolar and N = 23,760 bipolar EGM segments. We compared DL to traditional classifiers based on rate or regularity. We explained DL using computer models to assess the impact of controlled variations in shape, rate

SUBMITTER: Rodrigo M 

PROVIDER: S-EPMC9951584 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature