Explaining deep neural networks for knowledge discovery in electrocardiogram analysis

Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and
accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical
decision-making, any deep learning-based prediction should be accompanied by an explanation that
a human can understand. We present an approach called electrocardiogram gradient class activation
map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep
learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid
diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical
tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep
learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show
an example of how attention maps may be used to develop novel ECG features.