Machine Learning-Based Arrhythmia Classification

Abstract

The rising numbers of cardiovascular diseases (CVDs) have become a major health concern. Arrhythmia is a most deadly heart condition in all cardiovascular diseases. Thus, prompt and accurate diagnosis of patients with arrhythmia is important in preventing heart disease and sudden cardiac death. Arrhythmia can be detected by the presence on electrocardiogram (ECG) of an irregular heart electrical activity. The heart's electrical activity is recorded as an ECG signal which contains physiological and pathological information. Classification of the ECG signals is very important to automatically diagnose heart disease. This chapter addresses the various types of learning methods for automatically classifying different types of heart beats. Reported studies demonstrate that the convolutional neural network (CNN) model is supremely suggested for the classification of arrhythmia. The best classification accuracy of 99.88% is achieved by an ensemble of depthwise separable convolutional (DSC) neural networks.