用VAE误差识别心电图中的心肌瘢痕特征，辅助无磁共振筛查。
Learning from VAE Errors to support ECG-based Differential Diagnosis of Myocardial Scar
- 基于β-VAE学习正常心电图表示，通过重构误差捕捉异常模式。
- 12导联中10导联的重构误差在瘢痕患者与健康人间显著差异。
- 重构误差可作为新指标，适合临床心肌病早期筛查应用。
心脏磁共振延迟钆增强（LGE）是心肌瘢痕的关键标志，但其可及性有限，促使常规心电图（ECG）筛查的发展。本研究评估了基于β-变分自编码器（β-VAE）的心电图表征能否在300名受试者的本地队列中区分LGE+与LGE-心肌病患者。将基础模型ECGx.AI的32维特征与在正常PTB-XL心电图上训练的浅层β-VAE特征进行对比，评估下游分类性能及动态时间规整（DTW）重构误差。ECGx.AI在随机森林下达到0.686的ROC-AUC，而所提β-VAE在梯度提升下达到0.577的AUC，敏感度为0.775。值得注意的是，根据曼-惠特尼U检验，12导联中有10导联的DTW重构误差在两组间显著不同，且用于分类时在逻辑回归下获得0.643的ROC-AUC，支持其作为瘢痕相关心电图改变的潜在标记物。
原文摘要 · Abstract (English)
Late Gadolinium Enhancement (LGE) on cardiac magnetic resonance is a key marker of myocardial scar, but its limited accessibility motivates routine ECG-based screening. We evaluated whether $\beta$-variational autoencoder (VAE)-derived ECG representations can discriminate LGE+ from LGE- cardiomyopathic patients in a local cohort of 300 subjects. We compared 32-dimensional features from the foundation ECGx.AI model with those from a shallower $\beta$-VAE trained on normal PTB-XL ECGs, evaluating downstream classification and Dynamic Time Warping (DTW)-based reconstruction errors. ECGx.AI reached an area under ROC of 0.686 with Random Forest, while the proposed $\beta$-VAE reached 0.577 with sensitivity of 0.775 with Gradient Boosting. Notably, DTW-reconstruction errors significantly differed between classes in 10 out of 12 leads according to Mann-Whitney U test and help in classification, leading to an area under ROC of 0.643 with Logistic Regression, supporting their potential as markers of scar-related ECG alterations.
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