Characterising cardiac tissue properties with graph neural networks

2026-08-16

AI Summary

This study addresses the challenges of cardiac electrophysiological characterization and ablation target localization under sparse intracardiac measurements by proposing a graph neural network-based framework for sparse data representation. Through pre-training on synthetic signals to identify regions of interest for premature ventricular contraction ablation, combined with few-shot fine-tuning, the method achieves cross-domain generalization from planar to curved surfaces. Experimental results demonstrate average precisions of 0.96, 0.97, and 0.95 in detecting fibrosis, rapid depolarization, and high excitability, respectively. These findings indicate that the proposed approach effectively enables precise mapping despite clinical data sparsity, exhibiting superior generalization capabilities and significant potential for clinical translation in cardiac electrophysiology procedures.

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.

Problem

• Cardiac tissue characterization
• Electrophysiological properties
• Premature ventricular complexes
• Ablation target localization
• Sparse intracardiac measurements

Innovation

• Graph Neural Networks
• Few-shot Fine-tuning
• Cardiac Tissue Characterisation
• Premature Ventricular Complexes
• Spatially Sparse Measurements