Automated Detection of Posterior Myocardial Infarction From Vectorcardiogram Signals Using Fourier–Bessel Series Expansion Based Empirical Wavelet Transform

Abstract

Posterior myocardial infarction (PMI) is a fatal condition of the human heart, wherein the posterior coronary circulation becomes disrupted. If left untreated, the PMI can cause a severe heart attack. To detect PMI, the conventional 12-lead standard electrocardiogram signals demonstrate poor detection sensitivity due to the absence of posterior sensing electrodes. Contrastingly, the three lead vectorcardiogram (VCG) signal has the sensor electrodes placed on the posterior side, thereby improving the reliability of PMI diagnosis. In this letter, we use VCG signals for the classification of PMI and healthy control (HC) subjects. The abnormalities in the electrical conduction due to PMI are captured using Fourier–Bessel series expansion based empirical wavelet transform, followed by the singular value decomposition (SVD). The resultant feature space is then utilized to classify PMI and HC segments using a support vector machine (SVM) classifier with various kernels, namely, linear, Gaussian, and radial basis function (RBF). The SVM with RBF kernel has resulted in the maximum classification accuracy, sensitivity, and specificity of 95.52, 91.08, and 97.45%, respectively, over the Physikalisch-Technische Bundesanstalt diagnostic database. Thus, the proposed method has the potential to be used in the clinical setting for accurate and robust PMI detection.