  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%.
