A Vectorcardiogram-based Classification System for the Detection of Myocardial Infarction

Abstract

Myocardial infarction (MI), generally known as a heart attack, is one of the top leading causes of mortality in the world. In clinical diagnosis, cardiologists generally utilize 12-lead ECG system to classify patients into MI symptoms: 1. ST segment elevation, 2. ST segment depression or T wave inversion. However unstable ischemic syndromes have rapidly changing supply versus demand characteristics that is one of the several limitations of 12-lead ECG system for MI detection. In addition, the ECG sensor placements of 12-lead system is not easily donned and doffed for tele-healthcare monitoring at home. Vectorcardiogram (VCG) system in clinic is another type of diagnosis plot which represents the magnitude and direction of the electrical potential in the form of a vector loop during cardiac electric activity. The VCG system can easily acquire three ECG waves from X, Y, Z directions to composite vector signal in space and the VCG signals can be transferred to 12-lead ECG signal through Dower transformation and vice versa. Hence, this study attempts to develop a VCG-based classification system for the detection of Myocardial infarction. In the experiment results, the proposed system can select the proper ECG features based on cardiologist's knowledge and proposed principal moments of QRS complex. The classification performance of MI detection can be reached to 99.89% of sensitivity, 92.51% of specificity, 95.35% of positive predictive value, and 96.96% overall accuracy with maximum-likelihood classifier (MLC).