Arrhythmia Classification Using Deep Learning: A Review

In most hospitals, the diagnosis of medical disorders involves the traditional approach of doctors manually analyzing the medical reports of the patient. This method is not only time consuming and strenuous, but is also highly prone to human error. With the advent of deep learning technology, an efficient autonomous diagnosis method holds the possibility of replacing the existing tedious approach. This in turn results in the reduction of human error which is of major concern in the medical industry today. Through this paper, we aim to put forth an articulate review of the different deep learning methodologies, observed in the past four years, to classify arrhythmia using electrocardiogram (ECG) signals.