Artificial intelligence–enabled mobile electrocardiograms for event prediction in paroxysmal atrial fibrillation

ABSTRACT:

Background

Paroxysmal atrial fibrillation (AF) often eludes early diagnosis, resulting in significant morbidity and mortality. Artificial intelligence (AI) has been used to predict AF from sinus rhythm electrocardiograms (ECGs), but AF prediction using sinus rhythm mobile electrocardiograms (mECG) remains unexplored.

Objective

The purpose of this study was to investigate the utility of AI to predict AF events prospectively and retrospectively using sinus rhythm mECG data.

Methods

We trained a neural network to predict AF events from sinus rhythm mECGs obtained from users of the Alivecor KardiaMobile 6L device. We tested our model on sinus rhythm mECGs within ±0–2 days, ±3–7 days, and ±8–30 days from AF events to determine the optimal screening window. Finally, we

SUBMITTER: Raghunath A 

PROVIDER: S-EPMC9971999 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature