CONTRASTIVE HEARTBEATS: CONTRASTIVE LEARNING FOR SELF-SUPERVISED ECG REPRESENTATION AND PHENOTYPING

Crystal T. Wei*, Ming En Hsieh, Chien Liang Liu, Vincent S. Tseng

*此作品的通信作者

研究成果: Conference contribution同行評審

摘要

The non-invasive and easily accessible characteristics of electrocardiogram (ECG) attract many studies targeting AI-enabled cardiovascular-related disease screening tools based on ECG. However, the high cost of manual labels makes high-performance deep learning models challenging to obtain. Hence, we propose a new self-supervised representation learning framework, contrastive heartbeats (CT-HB), which learns general and robust electrocardiogram representations for efficient training on various downstream tasks. We employ a novel heartbeat sampling method to define positive and negative pairs of heartbeats for contrastive learning by utilizing the periodic and meaningful patterns of electrocardiogram signals. Using the CT-HB framework, the self-supervised learning model learns personalized heartbeat representations representing the specific cardiology context of a patient. Evaluations on public benchmark datasets and a private large-scale real-world dataset with multiple tasks demonstrate that the learned semantic representations result in better performance on downstream tasks and retain high performance while supervised learning suffers performance degradation with fewer supervised labels in downstream tasks.

原文English
主出版物標題2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1126-1130
頁數5
ISBN(電子)9781665405409
DOIs
出版狀態Published - 2022
事件47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore
持續時間: 23 5月 202227 5月 2022

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2022-May
ISSN(列印)1520-6149

Conference

Conference47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
國家/地區Singapore
城市Virtual, Online
期間23/05/2227/05/22

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