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Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning
April 9, 2024, 4:46 a.m. | Aofan Jiang, Chaoqin Huang, Qing Cao, Yuchen Xu, Zi Zeng, Kang Chen, Ya Zhang, Yanfeng Wang
cs.CV updates on arXiv.org arxiv.org
Abstract: The electrocardiogram (ECG) is an essential tool for diagnosing heart disease, with computer-aided systems improving diagnostic accuracy and reducing healthcare costs. Despite advancements, existing systems often miss rare cardiac anomalies that could be precursors to serious, life-threatening issues or alterations in the cardiac macro/microstructure. We address this gap by focusing on self-supervised anomaly detection (AD), training exclusively on normal ECGs to recognize deviations indicating anomalies. We introduce a novel self-supervised learning framework for ECG AD, …
abstract accuracy anomaly anomaly detection arxiv clinical computer costs cs.cv detection diagnosis diagnostic disease healthcare healthcare costs heart disease improving life macro precursors self-supervised learning supervised learning systems through tool type
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