Detection of myocardial infarction from vectorcardiogram using relevance vector machine

Abstract

Myocardial infarction is a coronary artery ailment, and it is characterized by the changes in the morphological features such as the shape of T-wave, Q-wave and ST-segment of ECG signal. In clinical standard, it is a challenging problem to diagnose MI pathology using 12-lead ECG and vectorcardiogram (VCG). VCG has the advantage to record the heart electrical activities in three orthogonal planes (frontal, sagittal and transverse). This paper proposes a new method for automated detection or grading of MI pathology from vectorcardiogram (VCG) signals. The method uses relevance vector machine (RVM) classifier and the multiscale features of VCG signal for MI detection. The multiscale analysis of VCG signal is performed using dual-tree complex wavelet transform. The diagnostic features such as the complex wavelet sub-band (CWS) \(L_{1}\)-Norm (CWS \(L_{1}\)-norm) and the complex wavelet entropy (CWE) are evaluated from the sub-band complex wavelet coefficients of each orthogonal lead of VCG. The RVM classifier is considered to evaluate the performance of the combination of the CWS \(L_{1}\)-norm and the CWE features of VCG. Three different kernel functions such as Gaussian, bubble and Cauchy are used for RVM. The results show that the RVM classifier with Gaussian kernel function has an average accuracy, an average sensitivity and an average specificity values of 99.80, 99.67 and 99.90%, respectively. The performance of RVM classifier is compared with the existing methods for detection of MI from VCG and 12-lead ECG signals.