Cardiovascular disorders, such as myocardial infarction (MI) are the leading causes of mortality in the world. This paper presents an approach that uses novel spatio-temporal patterns of the vectorcardiogram (VCG) signals for the identification of various types of MI. In contrast to the traditional electrocardiogram (ECG) approaches, the 3D cardiac VCG signal is partitioned into 8 octants for localized analysis of the heart's electrical activities. The proposed method was tested using the PhysioNet PTB database for 368 MIs and 80 healthy control (HC) recordings, each of which includes 12-lead ECG and 3-lead VCG. Significant differences are found in the VCG spatial distribution between MI and HC groups. Furthermore, classification and regression tree (CART) analysis was used to demonstrate that VCG octant features can distinguish MIs from HCs with a sensitivity (accuracy of MI identification) of 97.28% and a specificity (accuracy of HC identification) of 95.00%, which is promising compared to the previously reported results using other ECG databases. The results indicate that the present approach provides an effective way for monitoring, post-processing, and interpretation of ECG data, and hopefully can impact the current cardiac diagnostic practice.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering