Derived vectorcardiogram based automated detection of posterior myocardial infarction using FBSE-EWT technique

The early detection of posterior myocardial infarction (PMI) is an important task as it can cause cardiac failure. Due to the absence of extra posterior leads in the standard 12-lead electrocardiogram (ECG), the PMI detection sensitivity degrades. To improve it, additional posterior leads can be included in standard 12-lead ECG system. Another approach utilizing vectorcardiogram (VCG) has been in practice with specific 7 electrodes with one posterior lead. Although the VCG approach is promising, the arrangement of posterior lead causes patient discomfort. To overcome aforesaid issue, derived VCG (dVCG) obtained through the Dowers inverse transformation has been proposed, wherein, from the standard 12-lead ECG, a three lead VCG is derived, which then can be utilized for PMI detection. Relying on the dVCG approach, in the present paper, we have proposed a novel methodology for PMI detection using Fourier-Bessel series expansion based empirical wavelet transform (FBSEEWT). The non-stationary behavior of dVCG has been captured using FBSE-EWT, followed by the principal component analysis employing eigenvalues of covariance matrix. The constructed feature space is then fed to decision tree (DT), support vector machine (SVM), and K-nearest neighbor (KNN) classifiers. The KNN classifier with inverse squared city block distance resulted in the overall classification accuracy of 97.92% with sensitivity and specificity of 96.63% and 98.50%, respectively. The experiments were performed over PhysikalischTechnische Bundesanstalt Diagnostic (PTBD) dataset. The proposed method has the potential to be utilized in accurate and robust PMI detection from dVCG signals in the clinical settings.

Automated summary

A novel methodology for posterior myocardial infarction (PMI) detection using Fourier-Bessel series expansion based empirical wavelet transform (FBSEEWT) was proposed in this study. The derived VCG signals were analyzed using decision tree (DT), support vector machine (SVM), and K-nearest neighbor (KNN) classifiers, achieving an overall classification accuracy of 97.92%. This method shows potential for accurate and robust PMI detection in clinical settings.