ECGEFNet: A two-branch deep learning model for calculating left ventricular ejection fraction using electrocardiogram

Abstract

Left ventricular systolic dysfunction (LVSD) and its severity are correlated with the prognosis of cardiovascular diseases. Early detection and monitoring of LVSD are of utmost importance. Left ventricular ejection fraction (LVEF) is an essential indicator for evaluating left ventricular function in clinical practice, the current echocardiography-based evaluation method is not avaliable in primary care and difficult to achieve real-time monitoring capabilities for cardiac dysfunction. We propose a two-branch deep learning model (ECGEFNet) for calculating LVEF using electrocardiogram (ECG), which holds the potential to serve as a primary medical screening tool and facilitate long-term dynamic monitoring of cardiac functional impairments. It integrates original numerical signal and waveform plots derived from the signals in an innovative manner, enabling joint calculation of LVEF by incorporating diverse information encompassing temporal, spatial and phase aspects. To address the inadequate information interaction between the two branches and the lack of efficiency in feature fusion, we propose the fusion attention mechanism (FAT) and the two-branch feature fusion module (BFF) to guide the learning, alignment and fusion of features from both branches. We assemble a large internal dataset and perform experimental validation on it. The accuracy of cardiac dysfunction screening is 92.3%, the mean absolute error (MAE) in LVEF calculation is 4.57%. The proposed model performs well and outperforms existing basic models, and is of great significance for real-time monitoring of the degree of cardiac dysfunction.