Variational auto-encoders improve explainability over currently employed heatmap methods for deep learning-based interpretation of the electrocardiogram.

Abstract

We appreciate the opportunity to address Higaki and Yamaguchi and their detailed commentary on our study. 1 In the referenced study, we show that variational auto-encoders (VAEs), which use deep neural networks (DNNs) to learn the underlying factors of variation in the median beat electrocardiogram (ECG), can be used to provide improved explainability over previous attempts to open the ‘black box’ of ECG-based DNNs using saliency-based heatmaps. There are currently conflicting definitions of explainability and interpretability in the literature and both are used interchangeably