An ECG-Based Model for Left Ventricular Hypertrophy Detection: A Machine Learning Approach

Goal: Despite the high incidence of left ventricular hypertrophy (LVH), clinical LVH-electrocardiography (ECG) criteria remain unsatisfactory due to low sensitivity. We propose an automatic LVH detection method based on ECG-extracted features and machine learning. Methods: ECG features were automatically extracted from two publicly available databases: PTB-XL with 2181 LVH and 9001 controls, and Georgia with 1012 LVH and 1387 controls. After preprocessing and feature extraction, the most relevant features from PTB-XL were selected to train three models: logistic regression, random forest (RF), and support vector machine (SVM). These classifiers, trained with selected features and a reduced set of five features, were evaluated on the Georgia database and compared with clinical LVH-ECG criteria. Results: RF and SVM models showed accuracies above 90% and increased sensitivity to above 86%, compared to clinical criteria achieving 38% at maximum. Conclusions: Automatic ECG-based LVH detection using machine learning outperforms conventional diagnostic criteria, benefiting clinical practice.