Exploring Interpretable AI Methods for ECG Data Classification

We address ECG data classification, using methods from explainable artificial intelligence (XAI). In particular, we focus on the extended performance of the ST-CNN-5 model compared to established models. The model showcases slight improvement in accuracy suggesting the potential of this new model to provide more reliable predictions compared to other models. However, lower values of the specificity and area-under-curve metrics highlight the need to thoroughly evaluate the strengths and weaknesses of the extended model compared to other models. For the interpretability analysis, we use Shapley Additive Explanations (SHAP), Gradient-weighted Class Activation Mapping (GradCAM), and Local Interpretable Model-agnostic Explanations (LIME) methods. In particular, we show that the new model exhibits improved explainability in its GradCAM explanations compared to the former model. SHAP effectively highlights crucial ECG features, better than GradCAM and LIME. The latter methods exhibit inferior performance, particularly in capturing nuanced patterns associated with certain cardiac conditions. By using distinctive methods in the interpretability analysis, we provide a systematic discussion about which ECG features are better - or worse - uncovered by each method.