Deep learning for atrial electrogram estimation: toward non-invasive arrhythmia mapping using variational autoencoders.

ABSTRACT:

Background

Non-invasive estimation of intracardiac electrograms (EGMs) from body surface potential measurements (BSPMs) could reduce reliance on invasive mapping and enable safer, patient-specific characterization of atrial arrhythmias. Conventional inverse problem formulations, such as Tikhonov regularization, are limited by ill-posedness, sensitivity to anatomical inaccuracies, and low spatial resolution.

Objective

In this work, we propose a dual-branch deep learning (DL) architecture based on a variational autoencoder (VAE) to directly reconstruct atrial EGMs from BSPMs.

Methods

A dataset of 680 BSPM-EGM pairs was generated using biatrial computational models simulating a wide spectrum of rhythms, including sinus rhythm, atrial fibrillation (AF), ectopic activity,

SUBMITTER: Gutierrez-Fernandez M 

PROVIDER: S-EPMC12832759 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature