Abstract: In order to improve the accuracy of heart sound recognition of artificial intelligence assisted diagnosis, according to the periodicity of heart sound signal, we proposed a fast principal component analysis algorithm to reduce the dimension of heart sound signal and extract features. At the same time, based on the simplex evolution algorithm, the output of BP neural network learning algorithm and the expected error function were optimized to improve the learning performance of BP neural network and realize the classification and recognition of heart sound signal with higher accuracy. Aiming at the normal heart sound and eight kinds of abnormal heart sound signals, the performance was analyzed and tested. The experimental results show that the average recognition rate of all kinds of heart sounds is 95.96%. Compared with other algorithms, the improved algorithm improves the recognition rate by 4.9%, 3.9% and 1.9% respectively. It shows that the proposed algorithm can effectively classify and recognize heart sound signals and improve the recognition rate of artificial assisted diagnosis.

Key words: simplex evolution algorithm, fast principal component analysis, BP neural network, heart sound recognition