Synthetic Electrogram Generation with Variational Autoencoders for ECGI
This work addresses data scarcity in noninvasive cardiac imaging for atrial fibrillation assessment, but it is incremental as it applies existing generative methods to a specific domain problem.
The authors tackled the limited availability of paired body surface potential and intracardiac electrogram datasets for electrocardiographic imaging by using variational autoencoders to generate synthetic atrial electrograms, achieving higher fidelity with a sinus rhythm-specific model and moderate performance improvements in downstream reconstruction tasks through data augmentation.
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.