EFFNet: Efficient feature fusion network for left ventricular hypertrophy identification based on 12-lead electrocardiogram signals.

BACKGROUND: Left ventricular hypertrophy (LVH) is a common cardiovascular disorder, yet its detection from electrocardiogram (ECG) signals remains challenging because of the low sensitivity of conventional criteria. OBJECTIVE: We aimed to explore a new deep learning method for effective LVH identification based on 12-lead ECG signals. METHODS: We proposed a novel ECG-based efficient feature fusion network (EFFNet) for LVH classification, incorporating a novel feature fusion module that fuses morphological features extracted by a convolutional neural network with algorithm-derived amplitude features and a mixture of experts module. Experiments with 5-fold cross-validation were performed on the 12-lead ECG signals in the UK Biobank data set (n = 38,289), with cardiovascular magnetic resonance measurement serving as the reference standard. We also tested the model on an external cohort, the Qinghai data set, using echocardiography as the standard (n = 142,777). Using the UK Biobank and Qinghai cohorts, we assessed associations between EFFNet-predicted LVH and cardiovascular rhythm abnormalities. RESULTS: Experimental results with cross-validation on the UK Biobank data set showed that, on average, EFFNet achieved an area under the receiver operating characteristic curve of 0.933, which outperformed conventional ECG-based diagnostic methods and comparative deep learning methods. On the external Qinghai validation set, EFFNet achieved an area under the receiver operating characteristic curve of 0.654. EFFNet-predicted LVH was associated with atrial fibrillation, ventricular premature beats, and atrial premature beats. CONCLUSION: EFFNet significantly improves ECG-based LVH detection and introduces a novel deep learning framework for LVH risk prediction, advancing intelligent ECG-based screening and early identification of LVH.