ExECG: An Explainable AI Framework for ECG models

TL;DR

ExECG is a Python framework that standardizes, unifies, and visualizes explainability methods for ECG deep learning models to improve interpretability and reproducibility in clinical applications.

Contribution

The paper introduces ExECG, a comprehensive pipeline that standardizes data access, unifies explainability methods, and provides visualization tools for ECG model interpretability.

Findings

Demonstrates end-to-end usage with examples and case studies

Highlights interoperability and reproducibility benefits

Provides a unified interface for ECG explainability methods

Abstract

Deep learning has enabled ECG diagnostic models with strong performance in tasks such as arrhythmia classification and abnormality detection. However, accuracy alone is insufficient for clinical deployment because it does not explain why a specific output was produced, limiting justification, error analysis, and trust. Although ECG XAI has been extensively investigated and steadily improved, practical pipelines and reporting conventions vary across studies, hindering reuse and reproducibility. To address these issues, we present Explainable AI framework for ECG models (ExECG), a Python framework that provides a three-stage pipeline: Wrapper standardizes access across heterogeneous ECG formats and intermediate representations, Explainer unifies diverse XAI methods under a shared execution protocol, and Visualizer supports consistent cross-method comparison within a unified interface. We demonstrate end-to-end usage with concise examples and two case studies, highlighting interoperable and reproducible ECG explainability.