Explaining Black-Box Automated Electrocardiogram Classification to Cardiologists

In this work, we present a method to explain "end-to-end" electrocardiogram (ECG) signal classifiers, where the explanations were built along with seniors cardiologist to provide meaningful features to the final users. Our method focuses exclusively on automated ECG diagnosis and analyzes the explanation in terms of clinical accuracy for interpretability and robustness. The proposed method uses a noise-insertion strategy to quantify the impact of intervals and segments of the ECG signals on the automated classification outcome. An ECG segmentation method was applied to ECG tracings, to obtain: (1) Intervals, Segments and Axis; (2) Rate, and (3) Rhythm. Noise was added to the signal to disturb the ECG features in a realistic way. The method was tested using Monte Carlo simulation and the feature impact is estimated by the change in the model prediction averaged over 499 executions and a feature is defined as important if its mean value changes the result of the classifier. We demonstrate our method by explaining diagnoses generated by a deep convolutional neural network. The proposed method is particularly effective and useful for modern deep learning models that take raw data as input.