Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography
This addresses the problem of early MI detection for healthcare, but it is incremental as it applies existing one-class classification methods to a specific medical imaging domain.
The study tackled early detection of myocardial infarction (MI) using multi-view echocardiography by proposing a framework based on one-class classification techniques, achieving a sensitivity of 85.23% and F1-Score of 80.21% with a multi-modal approach.
Myocardial infarction (MI) is the leading cause of mortality and morbidity in the world. Early therapeutics of MI can ensure the prevention of further myocardial necrosis. Echocardiography is the fundamental imaging technique that can reveal the earliest sign of MI. However, the scarcity of echocardiographic datasets for the MI detection is the major issue for training data-driven classification algorithms. In this study, we propose a framework for early detection of MI over multi-view echocardiography that leverages one-class classification (OCC) techniques. The OCC techniques are used to train a model for detecting a specific target class using instances from that particular category only. We investigated the usage of uni-modal and multi-modal one-class classification techniques in the proposed framework using the HMC-QU dataset that includes apical 4-chamber (A4C) and apical 2-chamber (A2C) views in a total of 260 echocardiography recordings. Experimental results show that the multi-modal approach achieves a sensitivity level of 85.23% and F1-Score of 80.21%.