Challenging Black-Box Models: Interpretable Explanations for ECG Classification

Deep learning methods achieve high performance, while often lacking explainability, hindering application in the field. We propose the use of a logistic regression classifier based on temporal aligned Electrocardiograms, and the utilisation of interpretable feature importance. This work suggests that non-deep learning based classifiers achieve comparable performance, and introduce new opportunities to on-the-fly counterfactual explanations. The code, pretrained model, and extracted kernels are available under github.com/imi-ms/rlign.