Detecting Myocardial Infarction by Electrocardiogram Machine Learning Models with Greater Accuracy; A Technical Advance Article

Abstract

Background Electrocardiogram (ECG) interpretation is based on the understanding of cardiac electrical patterns. Machine learning (ML) techniques have been used to interpret ECGs, however, there is a lacuna in models able to identify the timing and affected cardiac territories with high accuracy. We aimed to utilize machine learning techniques coupled with relevant medical knowledge to create a machine learning model to detect MI with greater accuracy along with affected territory and timing. Methods A dataset containing 452 ECGs with 279 features from the University of California, Irvine, Machine Learning Repository was utilized. Three machine learning classification models namely Bootstrap Aggregation Decision Trees (BADT), Random Forest (RF) and Multi-layer Perceptron (MLP) were fed with ECG features selected based on the medical knowledge, categorized as normal, acute ischemic changes, old anterior MI and old inferior MI. Results The RF, BADT and MP models identified normal, acute ischemia, old anterior and old inferior MI with overall accuracies of 91.67% (95% CI: 84.24 – 96.33%), 89.58 (95% CI: 81.68 – 94.89%) and 85.42 (95% CI: 76.74 – 91.79%) respectively. All 3 models identified old anterior MIs with 100% sensitivity and specificity and RF model also identified old inferior MIs with same accuracy. Conclusion Machine learning models trained utilizing medical knowledge on the ECG changes in myocardial infarction can achieve greater accuracy in detecting MI along with affected area and timing. This study can be expanded by using a more extensive data set, to include more detail regarding timing and territories, as well as the detection and classification of arrhythmias.