  Electrocardiogram (ECG) can be reliably used as a measure to monitor the
functionality of the cardiovascular system. Recently, there has been a great
attention towards accurate categorization of heartbeats. While there are many
commonalities between different ECG conditions, the focus of most studies has
been classifying a set of conditions on a dataset annotated for that task
rather than learning and employing a transferable knowledge between different
tasks. In this paper, we propose a method based on deep convolutional neural
networks for the classification of heartbeats which is able to accurately
classify five different arrhythmias in accordance with the AAMI EC57 standard.
Furthermore, we suggest a method for transferring the knowledge acquired on
this task to the myocardial infarction (MI) classification task. We evaluated
the proposed method on PhysionNet's MIT-BIH and PTB Diagnostics datasets.
According to the results, the suggested method is able to make predictions with
the average accuracies of 93.4% and 95.9% on arrhythmia classification and MI
classification, respectively.
