  Heart disease is the number one killer, and ECGs can assist in the early
diagnosis and prevention of deadly outcomes. Accurate ECG interpretation is
critical in detecting heart diseases; however, they are often misinterpreted
due to a lack of training or insufficient time spent to detect minute
anomalies. Subsequently, researchers turned to machine learning to assist in
the analysis. However, existing systems are not as accurate as skilled ECG
readers, and black-box approaches to providing diagnosis result in a lack of
trust by medical personnel in a given diagnosis. To address these issues, we
propose a signal importance mask feedback-based machine learning system that
continuously accepts feedback, improves accuracy, and ex-plains the resulting
diagnosis. This allows medical personnel to quickly glance at the output and
either accept the results, validate the explanation and diagnosis, or quickly
correct areas of misinterpretation, giving feedback to the system for
improvement. We have tested our system on a publicly available dataset
consisting of healthy and disease-indicating samples. We empirically show that
our algorithm is better in terms of standard performance measures such as
F-score and MacroAUC compared to normal training baseline (without feedback);
we also show that our model generates better interpretability maps.
