Learning from VAE Errors to support ECG-based Differential Diagnosis of Myocardial Scar

2026-09-04

AI Summary

研究使用β-VAE从心电图中提取特征以区分有无心肌疤痕的患者，通过对比不同模型性能，发现DTW重建误差有助于提高分类效果。

Abstract

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 $β$-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 $β$-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 $β$-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.

Problem

• ECG
• Myocardial Scar
• LGE
• Differential Diagnosis

Innovation

• β-VAE
• Dynamic Time Warping (DTW)
• reconstruction errors
• LGE detection
• ECG-based diagnosis