用双生理对齐方法从心电图图像学到临床通用表征
Learning ECG Image Representations via Dual Physiological-Aware Alignments
- 通过图像与信号文本的多模态对比对齐学习表征
- 引入软导联约束提升导联间一致性，改善重建质量
- 在多个数据集上性能超越现有图像模型，接近信号分析水平
心电图（ECG）是心血管疾病诊断中最常用的工具之一，全球大量ECG数据仅以图像形式存在。然而，现有自动化ECG分析方法大多依赖原始信号记录，限制了其在真实世界和资源受限场景下的应用。本文提出ECG-Scan，一种基于双重生理感知对齐的自监督框架，从ECG图像中学习临床通用表征：1）通过图像与金标准信号-文本模态间的多模态对比对齐优化图像表征学习；2）引入软导联约束，正则化重建过程，提升信号导联间的一致性。在多个数据集和下游任务上的广泛基准测试表明，该图像模型性能优于现有图像基线，并显著缩小了图像与信号分析之间的差距。结果表明，自监督图像建模有望激活大规模遗留的ECG图像数据，推动自动化心血管诊断的普及。
原文摘要 · Abstract (English)
Electrocardiograms (ECGs) are among the most widely used diagnostic tools for cardiovascular diseases, and a large amount of ECG data worldwide appears only in image form. However, most existing automated ECG analysis methods rely on access to raw signal recordings, limiting their applicability in real-world and resource-constrained settings. In this paper, we present ECG-Scan, a self-supervised framework for learning clinically generalized representations from ECG images through dual physiological-aware alignments: 1) Our approach optimizes image representation learning using multimodal contrastive alignment between image and gold-standard signal-text modalities. 2) We further integrate domain knowledge via soft-lead constraints, regularizing the reconstruction process and improving signal lead inter-consistency. Extensive benchmarking across multiple datasets and downstream tasks demonstrates that our image-based model achieves superior performance compared to existing image baselines and notably narrows the gap between ECG image and signal analysis. These results highlight the potential of self-supervised image modeling to unlock large-scale legacy ECG data and broaden access to automated cardiovascular diagnostics.
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