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

Tutkimustuotos: KonferenssiartikkeliTieteellinenvertaisarvioitu

8 Sitaatiot (Scopus)
14 Lataukset (Pure)

Abstrakti

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 multimodal approach achieves a sensitivity level of 85.23% and F1-Score of 80.21%.
AlkuperäiskieliEnglanti
Otsikko2022 Computing in Cardiology (CinC)
KustantajaIEEE
Sivut1-4
Vuosikerta49
ISBN (elektroninen)979-8-3503-0097-0
DOI - pysyväislinkit
TilaJulkaistu - syysk. 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaComputing in Cardiology - Tampere, Suomi
Kesto: 4 syysk. 20227 syysk. 2022

Julkaisusarja

NimiComputing in Cardiology
KustantajaIEEE
ISSN (elektroninen)2325-887X

Conference

ConferenceComputing in Cardiology
Maa/AlueSuomi
KaupunkiTampere
Ajanjakso4/09/227/09/22

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