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 frame-work for early detection of MI over multi-view echocardio-graphy 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%.
| Original language | English |
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| Title of host publication | 2022 Computing in Cardiology, CinC 2022 |
| Publisher | IEEE Institute of Electrical and Electronic Engineers |
| Number of pages | 4 |
| ISBN (Electronic) | 979-8-3503-0097-0 |
| DOIs | |
| Publication status | Published - 2022 |
| MoE publication type | A4 Article in a conference publication |
| Event | 2022 Computing in Cardiology - Tampere, Finland Duration: 4 Sept 2022 → 7 Sept 2022 |
Conference
| Conference | 2022 Computing in Cardiology |
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| Country/Territory | Finland |
| City | Tampere |
| Period | 4/09/22 → 7/09/22 |
Funding
This study was supported in part by the NSF-Business Finland Center for Visual and Decision Informatics (CVDI) Advanced Machine Learning for Industrial Applications (AMaLIA) under Grant 4183/31/2021, and in part by the Haltian Stroke-Data projects.