Machine learning to classify left ventricular hypertrophy using electrocardiographic feature extraction by variational autoencoder.

Background: Traditional electrocardiographic (ECG) criteria for left ventricular hypertrophy (LVH) have modest diagnostic yield. Objective: This study aimed to develop and validate machine learning (ML) models for LVH diagnosis from ECG. Methods: ECG calculations (rate, intervals, and axis); R-wave, S-wave, and overall-QRS amplitudes; and QRS voltage-time integrals were obtained from 12-lead, vectorcardiographic X-Y-Z-lead, and 3-dimensional (root-sum-square) ECGs. Deep learning-enabled latent embeddings (30 per ECG) were extracted using a variational autoencoder (pretrained on unselected 1.18 million ECGs) from representative-beat signals. Logistic regression, random forest, light gradient boosted machine (LGBM), residual neural network and multilayered perceptron network models using ECG features (calculations and embeddings) and sex, and a convolutional neural network (CNN) using ECG signals alone were trained to predict LVH (left ventricular mass index, women >95 g/m Results: In the testing set (n = 54,984), the area under the receiver operating characteristic curve for LVH classification was higher for ML models using ECG features (LGBM 0.794; multilayered perceptron 0.793; residual neural network 0.795) than the best individual ECG variable (Z-axis QRS voltage-time integral 0.707), the best traditional criterion (Cornell voltage-duration product 0.716), and the CNN using ECG signals (0.788). Among patients without LVH who had a follow-up echocardiogram >1 year later, LGBM false positives, compared with true negatives, had a 3.07-fold higher odds of developing future LVH (95% confidence interval 2.44-3.86; Conclusion: ML models are superior to traditional ECG criteria for classifying LVH. Models trained on extracted ECG features, including deep-learning latent space representations, can outperform CNN models trained on ECG signals.