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CORRECTION article

Front. Physiol., 26 January 2026

Sec. Cardiac Electrophysiology

Volume 17 - 2026 | https://doi.org/10.3389/fphys.2026.1788430

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

This article is a correction to:

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

    1. Read original article
Miriam Gutirrez-Fernndez, Miriam Gutiérrez-Fernández1,2*K. Lpez-Linares,K. López-Linares2,3C. Fambuena-SantosC. Fambuena-Santos4Maria S. GuillemMaria S. Guillem4Andreu M. ClimentAndreu M. Climent4 . Barquero-PrezÓ. Barquero-Pérez1
  • 1Signal Theory and Communications Dpt., EIF, Universidad Rey Juan Carlos, Fuenlabrada, Spain
  • 2Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), San Sebastián, Spain
  • 3eHealth Group, Bioengineering Area, Biogipuzkoa Health Research Institute, San Sebastián, Spain
  • 4ITACA Institute, Universitat Politècnica de València, València, Spain

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

by Gutiérrez-Fernández M, López-Linares K, Fambuena-Santos C, Guillem MS, Climent AM and Barquero-Pérez Ó (2026). Front. Physiol. 16:1720244. doi: 10.3389/fphys.2025.1720244

In the published article, there was an error in the author Affiliations.

This correction addresses administrative errors in the published Affiliations and does not affect the scientific content of the article. Affiliations 2 and 3 were published incorrectly in the original article. The correct Affiliations are:

2Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), San Sebastián, Spain

3eHealth Group, Bioengineering Area, Biogipuzkoa Health Research Institute, San Sebastián, Spain

The original article has been updated.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: atrial fibrillation, body surface potential mapping, deep learning, inverse problem, variational autoencoder

Citation: Gutiérrez-Fernández M, López-Linares K, Fambuena-Santos C, Guillem MS, Climent AM and Barquero-Pérez Ó (2026) Correction: Deep learning for atrial electrogram estimation: toward non-invasive arrhythmia mapping using variational autoencoders. Front. Physiol. 17:1788430. doi: 10.3389/fphys.2026.1788430

Received: 15 January 2026; Accepted: 16 January 2026;
Published: 26 January 2026.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2026 Gutiérrez-Fernández, López-Linares, Fambuena-Santos, Guillem, Climent and Barquero-Pérez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Miriam Gutiérrez-Fernández, bWd1dGllcnJlemZAdmljb210ZWNoLm9yZw==

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.