Universal representations in cardiovascular ECG assessment: A self-supervised learning approach.

Autor: Liu ZY; Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan., Lin CH; Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan., Hsu YC; McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States., Chen JS; Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan., Chang PC; Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou and Chang Gung University Medical School, Taoyuan, Taiwan., Wen MS; Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou and Chang Gung University Medical School, Taoyuan, Taiwan., Kuo CF; Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan; Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital, Taoyuan, Taiwan. Electronic address: zandis@gmail.com.
Jazyk: angličtina
Zdroj: International journal of medical informatics [Int J Med Inform] 2024 Dec 01; Vol. 195, pp. 105742. Date of Electronic Publication: 2024 Dec 01.
DOI: 10.1016/j.ijmedinf.2024.105742
Abstrakt: Background: The 12-lead electrocardiogram (ECG) is an established modality for cardiovascular assessment. While deep learning algorithms have shown promising results for analyzing ECG data, the limited availability of labeled datasets hinders broader applications. Self-supervised learning can learn meaningful representations from the unlabeled data and transfer the knowledge to downstream tasks. This study underscores the development and validation of a self-supervised learning methodology tailored to produce universal ECG representations from longitudinally collected ECG data, applicable across a spectrum of cardiovascular assessments.
Methods: We introduced a pre-trained model that utilizes contrastive self-supervised learning to universal ECG representations from 4,932,573 ECG tracing from 1,684,298 adult patients on 7 campuses of Chang Gung Memorial Hospital. We extensively evaluated the proposed model using an internal dataset collected from diverse healthcare establishments and an external public dataset encompassing varied cardiovascular conditions and sample magnitudes.
Results: The pre-trained model showed the equivalent performance to the conventionally trained models, which solely rely on supervised learning in both internal and external datasets, to assess atrial fibrillation, atrial flutter, premature rhythm abnormalities, first-degree atrioventricular block, and myocardial infarction. When applied to small sample sizes, it was observed that the learned ECG representations enhanced the classification models, resulting in an improvement of up to 0.3 of the area under the receiver operating characteristic (AUROC).
Conclusions: The ECG representations learned from longitudinal ECG data are highly effective, particularly with small sample sizes, and further enhance the learning process and boost robustness.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier B.V.)
Databáze: MEDLINE
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