Myocardial Ischemia Detection Using Body Surface Potential Mappings and Machine Learning

Recent improvements in detecting acute myocardial ischemia via noninvasive body surface recordings have been driven by modern machine learning. While extensive research has been done using single and 12 lead ECGs, almost no models have incorporated body surface potential mappings. We created two contrasting machine learning models, logistic regression and XGBoost Classifier, and trained them on experimentally acquired body surface mappings with ground truth ischemia measurements recorded from within the heart. These models achieved a mean accuracy of 96.46% and 97.63%, as well as a mean AUC of 0.9927 and 0.9972 for the Logistic Regression and XGBoost classifiers, respectively. The anatomical location and relative contribution of each electrode were visualized and ranked. Then, new models were trained using data from only the top 12, 8, and 3 electrodes. These models trained on only a subset of the electrodes still exhibited relatively high accuracy and AUC, although at much faster training times.