# xGNN4MI: explainability of graph neural networks in 12-lead electrocardiography for cardiovascular disease classification

## TL;DR

This paper introduces xGNN4MI, an open-source framework for interpretable AI in ECG modeling to improve cardiovascular disease diagnosis.

## Contribution

The novel contribution is an XAI-integrated GNN framework for interpretable CVD prediction in 12-lead ECGs.

## Key findings

- The ECG-GNN models achieved good performance in CVD classification tasks.
- XAI analysis revealed clinically meaningful insights, such as differentiating types of myocardial infarction.

## Abstract

The clinical deployment of artificial intelligence (AI) solutions for assessing cardiovascular disease (CVD) risk in 12-lead electrocardiography (ECG) is hindered by limitations in interpretability and explainability. To address this, we present xGNN4MI, an open-source framework for graph neural networks (GNNs) in ECG modeling for interpretable CVD prediction. Our framework facilitates modeling clinically relevant spatial relationships between ECG leads and their temporal dynamics. We integrated explainable AI (XAI) and developed a task-specific XAI evaluation and visualization workflow to identify ECG leads crucial to the model’s decision-making process, enabling a systematic comparison with established clinical knowledge. We evaluated xGNN4MI on two challenging tasks: diagnostic superclass classification and localization of myocardial infarction. Our findings show that the interpretable ECG-GNN models demonstrate good performance across the tasks. XAI analysis revealed clinically meaningful training effects, such as differentiating between anteroseptal and inferior myocardial infarction. Our work demonstrates the potential of ECG-GNNs for providing trustworthy and interpretable AI-based CVD diagnosis.