Characterising cardiac tissue properties with graph neural networks

Ching-En Chiu; Yoo Ri Kim; Magdi Saba; Danilo Mandic; Marta Varela

q-bio.QMAug 16, 2026

Characterising cardiac tissue properties with graph neural networks

Authors: Ching-En Chiu, Yoo Ri Kim, Magdi Saba, Danilo Mandic, Marta Varela

Organizations: Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom · Department of Internal Medicine, Chonnam National University College of Medicine, Gwangju, Republic of Korea · St. George’s University Hospital, London, United Kingdom · Cardiovascular & Genomics Research Institute, City St George’s University of London, United Kingdom ·

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.

Figures & tables

Figure 1: A schematic of our proposed framework. The temporal encoder module based on a CNN learns latent representations of the EGM waveforms. Then, the GNN based spatial module propagates the latent information from sparsely distributed electrodes to high-resolution tissue nodes. A small classification head then takes the final embeddings and performs binary classification.

Low Resolution (LR) Model (CNN only)
	Average	Macro	Precision	Recall	ROC
	precision	F1			AUC
Fibrosis ( $D\downarrow$ ), single patch	0.76	0.80	0.45	0.96	0.990
Fibrosis ( $D\downarrow$ ), multiple patches	0.78	0.82	0.57	0.90	0.960
Rapid depolarisation ( $k\uparrow$ )	0.74	0.86	0.61	0.92	0.991
High excitability ( $a<0$ )	0.75	0.85	0.59	0.93	0.990

Table 1: Performance evaluated by average precision, macro F1, precision/recall (of the abnormal class at a decision threshold of 0.5), and ROC AUC for low resolution (LR) and GNN-based high resolution (HR) model.

Figure 2: Visualisation of the low resolution model and our proposed GNN-based high resolution model for detecting single & multi-patch lower $D$ , higher $k$ , and negative $a$ . The performance with sparse electrodes and noisy signals are also shown. Examples of healthy signals and signals from the pathological regions (in the locations indicated by a matching colour star) with and without noise are shown in the rightmost column.

Figure 3: Average precision of the HR model with increasing electrode sparsity and signal noise. Performance for both random and regular downsampling of electrodes are shown. Error bars are computed over 3 different rounds of random sampling.

Fibrosis ( $D\downarrow$ )
Metric	Lower curvature		Higher curvature
	(max $K$ = $2.23\times 10^{-3}~\text{mm}^{-2}$ )		(max $K$ = $1.19\times 10^{-2}~\text{mm}^{-2}$ )
	Zero-shot	Few-shot	Zero-shot	Few-shot
Average Precision	0.857	0.986	0.632	0.958
ROC-AUC	0.966	0.998	0.944	0.994
Max-F1 score	0.822	0.937	0.704	0.880

Table 2: Zero-shot and few-shot performance on curved surfaces.

Figure 4: Visualisation of focal pathology detection results after few-shot (10 graphs) fine-tuning on curved surfaces.

Graph Neural Networks

Graph Neural Networks learn from graph-structured data by combining information across connected nodes, edges, and larger structures.

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Physiology-Aware Graph Attention for Early Ventricular Reentry Prediction

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EPIONE · ISIR,MLIA

Graph Attention Networks

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Sudden cardiac death mostly originates from post-infarct ventricular tachycardia. This reentry is sustained by propagation around scar tissue, but its risk remains difficult to predict from infarct imaging alone. While in silico induction studies can reveal these mechanisms, exploring many substrates and pacing locations remains computationally expensive. We propose a graph attention classifier for early prediction of sustained ventricular reentry from simulated cardiac electrophysiology. Each simulation is represented as a graph with early nodal voltage samples and edge attributes encoding geometry, stimulation frequency, tissue labels, amplitude, and diastolic interval. These descriptors are computed only from the first two paced activations, before sustained reentry is observed. On a balanced independent test set of 1000 simulations, the classifier achieves 94.6% accuracy, 95.4% sensitivity, 93.8% specificity, 94.6% F1-score, 0.987 ROC-AUC, and 0.985 PR-AUC. Ablation experiments show that diastolic interval is the most critical descriptor. Qualitative attention inspection suggests that different heads focus on border-zone, stimulation location, and early propagation path. These results support graph attention as a fast early-prediction approach for large-scale in silico ventricular reentry studies.

Abstract

Graph Neural Networks

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