A Weighted SVM Based Approach for Automatic Detection of Posterior Myocardial Infarction Using VCG Signals

Myocardial infarction (MI), commonly known as heart attack is a life-threatening arrhythmia occurs due to insufficient oxygen supply to the heart tissues resulted from formation of clots in one or more coronary arteries. There is a growing interest among researchers for automatic detection of MI using computer algorithms. Based on the spatial location of damaged tissues MI is further categorized as anterior MI, septal MI, lateral MI, inferior MI and posterior MI. Among all, automatic detection of posterior MI (PMI) with standard 12-lead electrocardiogram (12-lead ECG) signal is challenging as it does not have monitoring electrodes posterior to human body. In this paper, we propose an automatic method for PMI detection using 3-lead vectorcardiogram (3-lead VCG) signal. The proposed approach exploits changes in electrical conduction properties of heart tissues during cardiac activity for healthy control (HC) and PMI subjects in three-dimensional (3D) space. To quantify these changes multiscale eigen features (MSEF) of subband matrices are used. Furthermore, we propose a cost sensitive weighted support vector machine (WSVM) classifier to combat class imbalance, which is a common problem in real-world disease data classification. The publicly available PhysioNet/PTBDB diagnostic database has been used to validate the proposed method by using a total of 1463 HC, and 148 PMI 4 sec 3-lead VCG signals. The best test accuracy of 96.69%, sensitivity of 80%, and geometric mean of 88.72% are achieved by WSVM classifier with radial basis function (RBF) kernel.