Characterising cardiac tissue properties with graph neural networks

Abstract:Characterising electrophysiological properties of cardiac tissue efficiently and accurately from spatially sparse intracardiac measurements is clinically important for localising ablation targets and improving arrhythmia treatment. We developed a graph neural network-based framework trained on synthetic electrogram signals on 2D flat surfaces to identify areas of interest in the context of cardiac ablation for premature ventricular complexes (PVCs). Our method achieved an average precision of 0.96, 0.97, and 0.95 for the detection of single-patch fibrosis, rapid depolarisation and high excitability, respectively. The trained model can then be applied to 2D curved surfaces with few-shot fine-tuning, demonstrating its generalisation capability. Future work will develop this framework further for clinical use in PVC ablation.

Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)

arXiv:2608.15843 [q-bio.QM]

(or arXiv:2608.15843v1 [q-bio.QM] for this version)

https://doi.org/10.48550/arXiv.2608.15843 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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