Explainable AI (XAI) for Arrhythmia detection from electrocardiograms

TL;DR

This paper explores the application of explainable AI techniques to ECG-based arrhythmia detection, emphasizing clinician-preferred explanations and comparing various XAI methods for clinical interpretability.

Contribution

It introduces domain-specific adaptations of XAI methods for ECG analysis and evaluates their effectiveness in clinical contexts.

Findings

Gradient-based and DeepLIFT methods provided more clinically relevant explanations.

Saliency maps were preferred by medical professionals for interpretability.

Model achieved 98.3% accuracy on MIT-BIH dataset.

Abstract

Advancements in deep learning have enabled highly accurate arrhythmia detection from electrocardiogram (ECG) signals, but limited interpretability remains a barrier to clinical adoption. This study investigates the application of Explainable AI (XAI) techniques specifically adapted for time-series ECG analysis. Using the MIT-BIH arrhythmia dataset, a convolutional neural network-based model was developed for arrhythmia classification, with R-peak-based segmentation via the Pan-Tompkins algorithm. To increase the dataset size and to reduce class imbalance, an additional 12-lead ECG dataset was incorporated. A user needs assessment was carried out to identify what kind of explanation would be preferred by medical professionals. Medical professionals indicated a preference for saliency map-based explanations over counterfactual visualisations, citing clearer correspondence with ECG interpretation workflows. Four SHapley Additive exPlanations (SHAP)-based approaches: permutation importance, KernelSHAP, gradient-based methods, and Deep Learning Important FeaTures (DeepLIFT), were implemented and compared. The model achieved 98.3% validation accuracy on MIT-BIH but showed performance degradation on the combined dataset, underscoring dataset variability challenges. Permutation importance and KernelSHAP produced cluttered visual outputs, while gradient-based and DeepLIFT methods highlighted waveform regions consistent with clinical reasoning, but with variability across samples. Findings emphasize the need for domain-specific XAI adaptations in ECG analysis and highlight saliency mapping as a more clinically intuitive approach