# Synthetic Electrogram Generation with Variational Autoencoders for ECGI

Source: [Synthetic Electrogram Generation with Variational Autoencoders for ECGI](<https://www.sota2.com/research/paper/6942392a45614693628506ee>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-20T05:00:10.481Z

Authors: Miriam Guti\\'errez-Fern\\'andez, Karen L\\'opez-Linares, Carlos Fambuena-Santos, Mar\\'ia S. Guillem, Andreu M. Climent, \\'Oscar Barquero-P\\'erez

Published: 2025-12-16T16:13:25.000Z

## Abstract

Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, and its clinical assessment requires accurate characterization of atrial electrical activity. Noninvasive electrocardiographic imaging (ECGI) combined with deep learning (DL) approaches for estimating intracardiac electrograms (EGMs) from body surface potentials (BSPMs) has shown promise, but progress is hindered by the limited availability of paired BSPM-EGM datasets. To address this limitation, we investigate variational autoencoders (VAEs) for the generation of synthetic multichannel atrial EGMs. Two models are proposed: a sinus rhythm-specific VAE (VAE-S) and a class-conditioned VAE (VAE-C) trained on both sinus rhythm and AF signals. Generated EGMs are evaluated using morphological, spectral, and distributional similarity metrics. VAE-S achieves higher fidelity with respect to in silico EGMs, while VAE-C enables rhythm-specific generation at the expense of reduced sinus reconstruction quality. As a proof of concept, the generated EGMs are used for data augmentation in a downstream noninvasive EGM reconstruction task, where moderate augmentation improves estimation performance. These results demonstrate the potential of VAE-based generative modeling to alleviate data scarcity and enhance deep learning-based ECGI pipelines.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Electrogram reconstruction from Body Surface Potential Maps on BSPM-EGM (test)](<https://www.sota2.com/research/sota/electrogram-reconstruction-from-body-surface-potential-maps-on-bspm-egm-test.md>): Electrogram reconstruction from Body Surface Potential Maps; BSPM-EGM (test); Correlation: 0.214.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2512.14537>)

[PDF](<https://arxiv.org/pdf/2512.14537.pdf>)