Heartbeat Classification and Arrhythmia Detection using Deep Learning.

Abstract

As per the reports of the World Health Organization (WHO), the number one cause of death today is cardiovascular diseases (CVDs). As per their statistics, the number of deaths caused by CVDs is roughly 30%. Cardiac Arrhythmia is a condition in which the electrical activity of the heart is abnormal. The electrical activity is very irregular leading to the disruption in the cardiac rhythm. In this work, we propose an efficient arrhythmia classification model by leveraging a 2-D Convolutional Neural Network (CNN). This work is based on the optimization of CNN using various techniques like batch normalization, data augmentation etc. This approach is unsupervised learning-based approach i.e. we have bypassed the conventional data pre-processing, feature extraction etc. and yet we achieve accuracy closer to 90% and more or less the same level of sensitivity. We also propose a method to classify the heartbeats which helps in classification of 5 different categories of arrhythmia which is compliant to the AAMI EC57 standards. This approach is based on t-sne model and we have been able to achieve an average accuracy closer to 93%.