Abstract
Characterising electrophysiological properties of cardiac tissue efficiently and accurately from spatially sparse intracardiac measure- ments 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 de- velop this framework further for clinical use in PVC ablation.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/STACOM2026_044.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/profile?id=~Ching-En_Chiu1. and https://openreview.net/profile?id=~Marta_Varela1
BibTex
@InProceedings{ChiChi_Characterising_MICCAISAT2026,
author = { Chiu, Ching-En AND Kim, Yoo Ri AND Saba, Magdi AND Mandic, Danilo AND Varela, Marta},
title = { { Characterising cardiac tissue properties with graph neural networks } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17267},
month = {pending},
page = {pending}
}