🤖 AI Summary
Current deep learning models for electrocardiogram (ECG) analysis face significant barriers to clinical deployment due to the absence of a standardized, reproducible framework for explainability. To address this challenge, this work proposes ExECG—the first end-to-end unified explainable AI framework specifically designed for ECG models. ExECG employs a three-layer architecture (Wrapper, Explainer, Visualizer) that supports diverse ECG data formats and integrates multiple explainable AI (XAI) algorithms. By standardizing data ingestion, enforcing a consistent analytical protocol, and providing a uniform visualization interface, the framework ensures interoperability across explanation methods and enhances result reproducibility. Case studies demonstrate that ExECG substantially improves the consistency of model explanations, clinical trustworthiness, and reusability in research settings.
📝 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.