The electrocardiogram (ECG) is routinely used in hospitals to analyze
cardiovascular status and health of an individual. Abnormal heart rhythms can
be a precursor to more serious conditions including sudden cardiac death.
Classifying abnormal rhythms is a laborious process prone to error. Therefore,
tools that perform automated classification with high accuracy are highly
desirable. The work presented classifies five different types of ECG arrhythmia
based on AAMI EC57 standard and using the MIT-BIH data set. These include
non-ectopic (normal), supraventricular, ventricular, fusion, and unknown beat.
By appropriately transforming pre-processed ECG waveforms into a rich feature
space along with appropriate post-processing and utilizing deep convolutional
neural networks post fine-tuning and hyperparameter selection, it is shown that
highly accurate classification for the five waveform types can be obtained.
Performance on the test set indicated higher overall accuracy (98.62%), as well
as better performance in classifying each of the five waveforms than hitherto
reported in literature.