用可控合成心电图预训练模型，提升小样本下心梗检测准确率
Improving Myocardial Infarction Detection via Synthetic ECG Pretraining
- 合成带可调心梗特征的12导联心电图，含真实噪声
- 预训练使低数据场景下心梗分类AUC最高提升4个百分点
- 适合临床数据少的心脏病检测研究者使用
心肌梗死是全球主要致死原因，从心电图（ECG）中实现精准早期诊断仍是临床重点。深度学习虽在自动心电图分析中展现潜力，但需大量标注数据，而实际中常难以获取。本文提出一种生理感知的流程：(i) 合成带有可调心梗形态和真实噪声的12导联心电图；(ii) 使用自监督掩码重建与联合重建-分类目标，对循环神经网络和变压器分类器进行预训练。通过统计与视觉分析验证了合成心电图的真实性，确认关键形态特征得以保留。在合成数据上预训练显著提升了分类性能，尤其在低数据条件下，AUC最高提升4个百分点。结果表明，在真实临床数据有限时，可控合成心电图有助于改善心梗检测效果。
原文摘要 · Abstract (English)
Myocardial infarction is a major cause of death globally, and accurate early diagnosis from electrocardiograms (ECGs) remains a clinical priority. Deep learning models have shown promise for automated ECG interpretation, but require large amounts of labeled data, which are often scarce in practice. We propose a physiology-aware pipeline that (i) synthesizes 12-lead ECGs with tunable MI morphology and realistic noise, and (ii) pre-trains recurrent and transformer classifiers with self-supervised masked-autoencoding plus a joint reconstruction-classification objective. We validate the realism of synthetic ECGs via statistical and visual analysis, confirming that key morphological features are preserved. Pretraining on synthetic data consistently improved classification performance, particularly in low-data settings, with AUC gains of up to 4 percentage points. These results show that controlled synthetic ECGs can help improve MI detection when real clinical data is limited.
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