Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteria

Abstract

Despite their remarkable performance, deep neural networks remain unadopted in clinical practice, which is considered to be partially due to their lack in explainability. In this work, we apply attribution methods to a pre-trained deep neural network (DNN) for 12-lead electrocardiography classification to open this "black box" and understand the relationship between model prediction and learned features. We classify data from a public data set and the attribution methods assign a "relevance score" to each sample of the classified signals. This allows analyzing what the network learned during training, for which we propose quantitative methods: average relevance scores over a) classes, b) leads, and c) average beats. The analyses of relevance scores for atrial fibrillation (AF) and left bundle branch block (LBBB) compared to healthy controls show that their mean values a) increase with higher classification probability and correspond to false classifications when around zero, and b) correspond to clinical recommendations regarding which lead to consider. Furthermore, c) visible P-waves and concordant T-waves result in clearly negative relevance scores in AF and LBBB classification, respectively. In summary, our analysis suggests that the DNN learned features similar to cardiology textbook knowledge.

arXiv commentAccepted version including new experiments on additional database

References in corpus (7)

• Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
• Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
• Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
• Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
• Automatic diagnosis of the 12-lead ECG using a deep neural network
• Leakage and the Reproducibility Crisis in ML-based Science
• ECG Beat Representation and Delineation by means of Variable Projection

Cited by in corpus (2)

• Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery
• Learning ECG Signal Features Without Backpropagation Using Linear Laws