Cardiac arrhythmia specifies uncommon electrical impulses of the heart that may be a major threat to humans. It
should be reported for clinical evaluation and care. Electrocardiogram monitoring (ECG) measurements perform a significant
part in the treatment of heart failure. Due to heartrate differences between individual patients and unknown disturbances in the
ECG readings it is difficult for doctors to identify the type of arrhythmia. Classification plays an important role in health
protection and computational biology. In this work, we aim to classify the heartbeats extracted from an ECG using deep
learning, based only on the line shape (morphology) of the individual heartbeats.
