ArticlemedRxiv : the preprint server for health sciences2025
Machine learning to classify left ventricular hypertrophy using ECG feature extraction by variational autoencoder.
Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Traditional ECG criteria for left ventricular hypertrophy (LVH) have modest diagnostic yield. Objective: Develop and validate machine learning models for LVH diagnosis from ECG. Methods: ECG summary features (rate, intervals, axis), R-wave, S-wave and overall-QRS amplitudes, and QRS voltage-time integrals (VTI Results: In the test set (n=54,984), AUC for LVH classification was higher for ML models using ECG features (LGBM 0.794, MLP 0.793, ResNet 0.795) compared with the best individual ECG variable (VT Conclusions: ML models are superior to traditional ECG criteria to classify LVH. Models trained on extracted ECG features, including latent variational autoencoder representations, can outperform CNN models directly trained on ECG signals.
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