A Lightweight Method of Myocardial Infarction Detection and Localization From Single Lead ECG Features Using Machine Learning Approach

Computerized myocardial infarction (MI) detection and localization can be useful for early prevention of its aggravation and related cardiac health complications. However, the published research either focuses on binary classification or implements complex classifiers for localization to achieve good accuracy. In this letter, the objective is to implement an 11-class MI localization system on resource-constrained hardware with low complexity and latency. A simple and optimized autoencoder-k-NN classifier has been used to achieve accuracy and F1-score of 99.74% and 99.20%, respectively, while evaluating single lead Electrocardiogram (ECG) features from the PTB-Diagnostic ECG database. A standalone hardware implementation with an ARM-v6-based controller resulted in a latency and runtime memory engagement of 0.48 s and 4.31 MB, respectively, to process 5 s ECG data. The present research can be useful for quick screening of MI for portable healthcare applications.