Single-Beat Myocardial Infarction Detection and Localization Using PSO-Optimized Extra Trees on 15-Lead ECG

With the advancement of wearable healthcare devices in recent times, there is a growing potential to revolutionize the current strategies of the detection of cardiac anomalies such as myocardial infarction (MI), making it viable to monitor out-of-the-hospital patients using conventional as well as unconventional and pseudo-electrocardiogram (ECG) leads. However, most of the MI detection and localization techniques which are available in the literature to date are based on either all the 12 conventional ECG leads, or subsets of thereof. A few single-lead ECG-based techniques are also there, but their accuracies are not satisfactory. On the contrary, this paper demonstrates a machine learning model that classifies MI from a single ECG beat of any of the 15 ECG-leads, including the 12 conventional leads and 3 vectorcardiogram (VCG)-leads, as available in the publicly accessible Physikalisch-Technische Bundesanstalt (PTB) Diagnostic ECG Database. In this proposed technique, first, the R-peaks are detected, and the arrhythmic beats are identified and excluded using a Ramanujan filter bank-based periodicity estimation technique. Next, a few hand-crafted features are extracted from the detected ECG beats and a set of important features are identified. Finally, Extra Trees classifier-based machine learning models with optimized hyperparameters, obtained using the particle swarm optimization technique, are developed utilizing these hand-crafted features for the detection and localization of MI. High F1-score and Matthew’s correlation coefficient corroborate the reliability of the proposed algorithm in both intra- and inter-patient paradigms.