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Related Experiment Video

Updated: Jan 29, 2026

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
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Deep learning for atrial electrogram estimation: toward non-invasive arrhythmia mapping using variational

Miriam Gutiérrez-Fernández1,2, K López-Linares2,3, C Fambuena-Santos4

  • 1Signal Theory and Communications Dpt., EIF, Universidad Rey Juan Carlos, Fuenlabrada, Spain.

Frontiers in Physiology
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model to non-invasively estimate intracardiac electrograms (EGMs) from body surface potential measurements (BSPMs). The novel approach improves atrial arrhythmia characterization, offering a safer alternative to invasive mapping.

Keywords:
atrial fibrillationbody surface potential mappingdeep learninginverse problemvariational autoencoder

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Area of Science:

  • Biomedical Engineering
  • Computational Cardiology
  • Artificial Intelligence in Medicine

Background:

  • Non-invasive estimation of intracardiac electrograms (EGMs) from body surface potential measurements (BSPMs) offers a safer alternative to invasive cardiac mapping for characterizing atrial arrhythmias.
  • Conventional methods like Tikhonov regularization struggle with ill-posedness, anatomical inaccuracies, and low spatial resolution.

Purpose of the Study:

  • To develop and validate a dual-branch deep learning (DL) architecture, specifically a variational autoencoder (VAE), for direct reconstruction of atrial EGMs from BSPMs.
  • To overcome limitations of traditional inverse problem formulations in non-invasive electrogram estimation.

Main Methods:

  • A dataset of 680 biatrial computational model-generated BSPM-EGM pairs simulating diverse rhythms (sinus, AF, ectopic, fibrotic) was utilized.
  • A VAE-based DL network was trained to learn a shared latent representation for simultaneous BSPM self-reconstruction and EGM prediction.
  • Performance was evaluated using temporal, spectral, voltage, and phase mapping metrics across baseline and extended datasets.

Main Results:

  • Stratified training demonstrated the most balanced performance, especially for atrial fibrillation (AF), enhancing correlation, peak detection precision, and spectral coherence.
  • The proposed DL model significantly outperformed the zero-order Tikhonov method in preserving waveform morphology and spectral content.
  • The DL approach successfully captured physiologically relevant temporal and spatial dynamics from BSPMs.

Conclusions:

  • Non-invasive, data-driven reconstruction of EGMs using DL is feasible and effective for capturing complex cardiac dynamics.
  • This approach provides more coherent functional information from BSPMs, potentially aiding in individualized diagnosis and guiding ablation strategies for atrial arrhythmias.
  • Deep learning offers a promising avenue for advancing non-invasive cardiac electrophysiology and patient-specific arrhythmia management.