Residual neural networks based on empirical mode decomposition for mitral regurgitation prediction

Abstract

A multi-modal residual neural network based on empirical mode decomposition (EMD) was proposed in this work and used for screening patients with mitral regurgitation (MR). EMD was used to decompose the synchronous electrocardiogram (ECG) and phonocardiogram (PCG) signals, and then the component with the highest degree of correlation with the original signals was selected for reconstruction, and then the reconstructed signals were converted into images by gramian angular difference field (GADF). Finally, the residual neural network was used to get the prediction results. In the present work, we established a database called Synchronized ECG and PCG Database for Patients with Mitral Regurgitation (SEP-MRDb) consisting of 1046 synchronous ECG and PCG recordings from patients with MR (n = 56) and without MR (n = 990). The experimental results showed that the accuracy rate of the proposed model was 96.90%, the precision was 97.10%, and recall was 97.10%, which can effectively capture the combined information of ECG and PCG signals and realize the recognition of MR.