通过学习与患者身份无关的特征，提升心电图异常检测效果
Finding "Good Views" of Electrocardiogram Signals for Inferring Abnormalities in Cardiac Condition
- 设计多种正样本匹配策略，基于时空不变性与波形特征构建
- 发现对患者身份不变的表示能显著提升心律失常分类性能
- 适用于医疗诊断中需泛化到新患者的深度学习模型开发
心电图（ECG）是筛查心脏异常的重要手段。近年来，深度学习已能在原始ECG信号上直接检测心律失常。尽管已有对比学习方法取得成功，但如何定义正样本仍无定论。本研究探索了多种正样本构造方式：时空不变性、通用数据增强、人口统计学相似性、心律特征及波形属性。通过下游心律失常分类任务评估，发现对患者身份不变的表示具有更强判别力。实验表明，此类表示在多个数据集上均表现优异，代码已开源。
原文摘要 · Abstract (English)
Electrocardiograms (ECGs) are an established technique to screen for abnormal cardiac signals. Recent work has established that it is possible to detect arrhythmia directly from the ECG signal using deep learning algorithms. While a few prior approaches with contrastive learning have been successful, the best way to define a positive sample remains an open question. In this project, we investigate several ways to define positive samples, and assess which approach yields the best performance in a downstream task of classifying arrhythmia. We explore spatiotemporal invariances, generic augmentations, demographic similarities, cardiac rhythms, and wave attributes of ECG as potential ways to match positive samples. We then evaluate each strategy with downstream task performance, and find that learned representations invariant to patient identity are powerful in arrhythmia detection. We made our code available in: https://github.com/mandiehyewon/goodviews_ecg.git
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。