Machine Learning-based Classification of Ischemic and Non-Ischemic Exercise Stress Test ECG

Abstract

Myocardial Ischemia (MI) is a fatal heart condition due to insufficient blood flow in the heart muscles, which may cause unexpected heart attacks. Exercise Stress Test (EST) Electrocardiogram (ECG) is a non-invasive diagnostic procedure that can help identify various disease conditions, including MI. This study aims to classify the ischemic and non-ischemic EST ECG using Machine Learning (ML) algorithms. EST ECGs for 152 patients (n=53 female) of mean age ($50 \pm 11.92$ years) were used in this study. ST morphology changes, measured during pre-load, load, and recovery at $J+(40$, 60, and 80 ms) were utilized as input to 14 ML classifiers. 70% of the input data to the ML classifiers were considered as train data, and 30% of the input data as test. Random Forest (RF) was selected based on the most suitable output and was used to classify between ischemic and non-ischemic by considering the clinical features such as ST variations, Blood Pressure (BP), Metabolic equivalent (Mets), and Rate Pressure Product (RPP) as input for both lead-II and V5. The model accuracy, sensitivity, precision, and F1 score for lead-II were 93%, 89.17%, 93%, and 89.63%, respectively. For V5, the performance matrices were 91%, 80%, 95%, and 86.14%, respectively.