  Cardiovascular disease is a large worldwide healthcare issue; symptoms often
present suddenly with minimal warning. The electrocardiogram (ECG) is a fast,
simple and reliable method of evaluating the health of the heart, by measuring
electrical activity recorded through electrodes placed on the skin. ECGs often
need to be analyzed by a cardiologist, taking time which could be spent on
improving patient care and outcomes. Because of this, automatic ECG
classification systems using machine learning have been proposed, which can
learn complex interactions between ECG features and use this to detect
abnormalities. However, algorithms built for this purpose often fail to
generalize well to unseen data, reporting initially impressive results which
drop dramatically when applied to new environments. Additionally, machine
learning algorithms suffer a "black-box" issue, in which it is difficult to
determine how a decision has been made. This is vital for applications in
healthcare, as clinicians need to be able to verify the process of evaluation
in order to trust the algorithm. This paper proposes a method for visualizing
model decisions across each class in the MIT-BIH arrhythmia dataset, using
adapted saliency maps averaged across complete classes to determine what
patterns are being learned. We do this by building two algorithms based on
state-of-the-art models. This paper highlights how these maps can be used to
find problems in the model which could be affecting generalizability and model
performance. Comparing saliency maps across complete classes gives an overall
impression of confounding variables or other biases in the model, unlike what
would be highlighted when comparing saliency maps on an ECG-by-ECG basis.
