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Cardiac arrhythmia is a prevalent and significant cause of morbidity andmortality among cardiac ailments. Early diagnosis is crucial in providingintervention for patients suffering from cardiac arrhythmia. Traditionally,diagnosis is performed by examination of the Electrocardiogram (ECG) by acardiologist. This method of diagnosis is hampered by the lack of accessibilityto expert cardiologists. For quite some time, signal processing methods hadbeen used to automate arrhythmia diagnosis. However, these traditional methodsrequire expert knowledge and are unable to model a wide range of arrhythmia.Recently, Deep Learning methods have provided solutions to performingarrhythmia diagnosis at scale. However, the black-box nature of these modelsprohibit clinical interpretation of cardiac arrhythmia. There is a dire need tocorrelate the obtained model outputs to the corresponding segments of the ECG.To this end, two methods are proposed to provide interpretability to themodels. The first method is a novel application of Gradient-weighted ClassActivation Map (Grad-CAM) for visualizing the saliency of the CNN model. In thesecond approach, saliency is derived by learning the input deletion mask forthe LSTM model. The visualizations are provided on a model whose competence isestablished by comparisons against baselines. The results of model saliency notonly provide insight into the prediction capability of the model but alsoaligns with the medical literature for the classification of cardiacarrhythmia.

Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia Classification