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

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 $\beta$-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 this http URL 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. this http URL 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.

Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2609.05294 [cs.LG]

(or arXiv:2609.05294v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.05294 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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