关键词: 先心病, 深度学习, 机器辅助听诊, 卷积神经网络

Abstract: Cardiac auscultation is the basic way for primary diagnosis and screening of Congenital Heart Disease（CHD）. In this paper, 1, 800 heart sound signals are used to train the mainstream deep learning networkssuch as RNN（Recurrent Neural Network）, LSTM （Long Short-Term Memory network）, and CNN（Convolutional Neural Networks）. The result shows that the RNN is prone to over-fitting. The LSTM network losses value of 0.257 and the accuracy of 0.872. The CNN losses value of 0.25 and the accuracy of 0.896. The CNN has greater potential than other two networks. The classification algorithm of CHD based on CNN has the universal applicability due to big training samples. Compared with ordinary networks the CNN has an advantage that is it can extract features automatically. This work is hopeful to be used in machine assisted auscultation of CHD.

Key words: Congenital Heart Disease, deep learning, machine-assisted auscultation, convolutional neural network