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

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.