Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography

TL;DR

This paper presents a novel framework using one-class classification over multi-view echocardiography data for early detection of myocardial infarction, addressing data scarcity issues and achieving high sensitivity.

Contribution

It introduces a multi-view, one-class classification approach for early MI detection in echocardiography, which is novel in handling limited datasets effectively.

Findings

Multi-modal approach achieves 85.23% sensitivity.

F1-Score of 80.21% with multi-view data.

Addresses data scarcity in MI detection.

Abstract

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%.