Classification of Multi-Lead ECG Signals to Predict Myocardial Infarction Using CNN

Abstract

Myocardial infarction (MI) which causes the damage to heart muscles and it lead to the critical stage of death. However the efficacious diagnosis of myocardial infarction (heart attacks) is needed for the healthy life of human. Electrocardiogram (ECG) is utilized to diagnose MI. A genuine time signal provides the electrical activities that are the subsidiary information about the functioning of heart. The expeditious and precise diagnose of MI need to be done with artificial intelligence based on computer aided techniques. In this paper, a multi layer deep convolutional neural network structure is proposed along with the data augmentation technique for the prediction of myocardial infarction. Furthermore, the implementation is done by using GPU version. When it comes to training and developing the new models and algorithms, the performance is determined by means of training and testing speed. Since GPU processor have been used to increase the computations speed and it also scales better then CPU.