The invention provides a
mitral valve regurgitation severity assessment method based on PCG signals, which comprises the following steps: firstly, synchronously acquiring signals from a heart sound area and an environmental background by adopting a dual-channel audio acquisition technology, eliminating
environmental noise and operation interference (such as clothes friction and non-uniform pressing force of a
stethoscope) through a self-adaptive
noise elimination algorithm, and calculating the severity of the
mitral valve regurgitation severity; and carrying out segmentation
processing on continuous heart sound signals of the de-noised heart sound by adopting a dynamic time window segmentation strategy to generate short-time heart sound fragments, evaluating the quality of the short-time heart sound fragments in real time by using a model based on a lightweight
convolutional neural network, and filtering low-quality fragments. Then, nonlinear features are directly extracted from the short-time heart sound fragments through a deep neural
network model based on a
clique block, after each effective fragment is subjected to four classifications, classification results of all the fragments are integrated through a majority voting mechanism, and final
mitral valve regurgitation severity judgment is generated. The classification robustness is effectively improved through multi-fragment
information fusion, and the
random error of single-fragment analysis is reduced.