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

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_QRS) were extracted from 12-lead, vectorcardiographic X-Y-Z-lead, and 3D (L2 norm) representative-beat ECGs. Latent features (30 per ECG) were extracted using a variational autoencoder (trained on unselected >1 million ECGs) from X-Y-Z-lead representative-beat ECG signals. Logistic regression, random forest, light gradient boosted machine (LGBM), residual network (ResNet) and multilayer perceptron network (MLP) models using ECG features and se

SUBMITTER: Gupta A 

PROVIDER: S-EPMC11527075 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature