Automated posterior myocardial infarction detection from vectorcardiogram and derived vectorcardiogram signals using MVFBSE-EWT method

Abstract

It is important to recognize and treat any sign or symptom of posterior myocardial infarction (PMI) promptly. The delay in the diagnosis of PMI may lead to heart failure. Because the standard 12-lead electrocardiogram (ECG) system does not have additional posterior leads, the PMI detection rate using standard 12-lead ECG is low. To improve the diagnostic performance of 12-lead ECG system, additional posterior leads can be added in the existing system. The addition of extra posterior leads may hamper patient comfort and aids in making cardiac monitoring complex. There exist two approaches to address the aforementioned issue. First approach utilizes Frank lead or vectorcardiogram (VCG), wherein, three signals obtained from seven electrodes have been used to record the cardiac activity. In the second approach, the Dowers inverse transform has been used to get derived VCG (dVCG) signal from the standard 12-lead ECG. In the present article, we have employed both the approaches (VCG and dVCG) to detect the PMI using multivariate Fourier-Bessel series expansion based empirical wavelet transform (MVFBSE-EWT). The entropy and complexity features have been extracted from multivariate Fourier-Bessel intrinsic mode functions (MVFBIMFs). The feature space has been reduced using artificial bee colony (ABC) optimization algorithm. Over the reduced feature set, the performance of three hypertuned classifiers, namely, support vector machine (SVM), k-nearest neighbor (KNN), and decision tree (DT) has been compared. The KNN classifier with group k-fold cross-validation strategy proves to be effective in classifying PMI and healthy control (HC) subjects for VCG and dVCG signals with an accuracy of 99.69 % and 99.55 %, respectively. Thus, the proposed method has the potential to enhance PMI detection accuracy without compromising patient comfort, promising practical improvements in clinical diagnostics. © 2025 Elsevier Inc.