Detection of myocardial infarction using analysis of vectorcardiographic loops

Abstract

Vectorcardiography is an alternative form of ECG for measuring electrical activity of the heart. It achieves higher sensitivity and provides the cardiologist additional information that can contribute to early diagnosis. This study is focused on proposal of a methodology for the processing of directly measured and transformed VCG records by using Kors regression transformation. A total 16 VCG features were extracted, while 12 features showed relevant information based on the statistical analysis and the method of maximum relevance minimum redundancy. These features served as input to the LDA and decision trees classifiers, while LDA achieved the most accurate results with accuracy 91.5%, specificity 76.3% and sensitivity 94.8% for directly measured VCG and accuracy 90.9%, specificity 76.3% and sensitivity 94.0% for transformed VCG. We conclude that this proposed methodology and the results obtained from it can be beneficial for the early diagnosis of myocardial infarction within the framework of automated detection.