标题： 通过合成心电图预训练提高心肌梗死检测 显示英文标题

标题： Improving Myocardial Infarction Detection via Synthetic ECG Pretraining

摘要： 心肌梗死是全球死亡的主要原因，从心电图（ECGs）中进行准确的早期诊断仍然是临床优先事项。深度学习模型在自动化心电图解释方面显示出前景，但需要大量标记数据，而这些数据在实践中往往稀缺。我们提出了一种生理感知的流程，(i) 合成具有可调MI形态和现实噪声的12导联心电图，并且 (ii) 使用自监督的掩码自动编码加上联合重建-分类目标对循环和变压器分类器进行预训练。我们通过统计和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到了保留。在合成数据上进行预训练始终提高了分类性能，特别是在数据量少的情况下，AUC提高了多达4个百分点。这些结果表明，当真实临床数据有限时，受控的合成心电图可以帮助改善MI检测。 显示英文摘要

摘要： 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.