Coronary Artery Disease Identification Using ECG: An Improved Energy Estimation-Based Approach

Abstract

Coronary Artery Disease (CAD) is a key factor in several serious heart conditions, such as ischemic heart disease, myocardial infarction, and heart failure. Detecting and treating CAD early is crucial for preventing further progression of the disease. Computer-aided techniques are needed to automate the characterization of CAD conditions. In this paper, a novel approach for the automated identification of CAD utilizing improved energy estimation of electrocardiogram (ECG) signals is developed. Due to the multi-component nature of ECG and the nonlinear characteristics of energy operators, estimating their instantaneous energy may lead to cross-component interactions complicating the interpretation. To overcome this issue, variational mode decomposition with a data-driven and adaptively initialized number of modes is suggested to estimate the accurate energy of ECG beat. In this process, the instantaneous energy of each decomposed component is computed for enhanced energy estimation of each ECG beat. Multiple statistical features calculated from the localized region of ECG beat could effectively classify CAD-affected ECG beats with more than 99.80 % accuracy with an optimized ensemble classifier and a 10-fold cross validation scheme. Moreover, experimental findings have revealed the effectiveness of the proposed methodology when compared to contemporary approaches employing identical datasets.

Keywords

• Coronary artery disease (CAD)
• electrocardiogram (ECG)
• energy operators
• machine learning and variational mode decomposition (VMD)