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arXiv Machine Learning By Shayan Sharifi, Riccardo Treu, Ilaria Gandin, Federico Garoia, Marco Merlo, Giulia Cisotto

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

Read the original on arXiv Machine Learning →

The study investigates whether ECG representations derived from a β-variational autoencoder (VAE) can distinguish patients with myocardial scar (LGE+) from those without (LGE-) using routine ECG data. In a cohort of 300 cardiomyopathic patients, the β-VAE achieved an AUC of 0.577 (sensitivity 0.775) with Gradient Boosting, while the foundation ECGx.AI model reached an AUC of 0.686 with Random Forest. Dynamic Time Warping reconstruction errors differed significantly between classes in most leads and improved classification to an AUC of 0.643 with Logistic Regression, suggesting these errors could serve as markers of scar-related ECG changes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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