  Cardiac arrhythmia is a prevalent and significant cause of morbidity and
mortality among cardiac ailments. Early diagnosis is crucial in providing
intervention for patients suffering from cardiac arrhythmia. Traditionally,
diagnosis is performed by examination of the Electrocardiogram (ECG) by a
cardiologist. This method of diagnosis is hampered by the lack of accessibility
to expert cardiologists. For quite some time, signal processing methods had
been used to automate arrhythmia diagnosis. However, these traditional methods
require expert knowledge and are unable to model a wide range of arrhythmia.
Recently, Deep Learning methods have provided solutions to performing
arrhythmia diagnosis at scale. However, the black-box nature of these models
prohibit clinical interpretation of cardiac arrhythmia. There is a dire need to
correlate the obtained model outputs to the corresponding segments of the ECG.
To this end, two methods are proposed to provide interpretability to the
models. The first method is a novel application of Gradient-weighted Class
Activation Map (Grad-CAM) for visualizing the saliency of the CNN model. In the
second approach, saliency is derived by learning the input deletion mask for
the LSTM model. The visualizations are provided on a model whose competence is
established by comparisons against baselines. The results of model saliency not
only provide insight into the prediction capability of the model but also
aligns with the medical literature for the classification of cardiac
arrhythmia.
