关键词: 脉象, 无阈值递归图, 卷积神经网络, 非线性分析

Abstract:

In the wrist pulse analysis and recognition, non-linear information of wrist pulse signal is difficult to be obtained by time domain or frequency domain analysis methods. Traditional machine learning methods use features extracted manually and cannot realize self-learning of features. A wrist pulse signal analysis and recognition method based on non-threshold recurrence plot and convolutional neural network has been proposed. Based on the nonlinear dynamics theory, the wrist pulse signals are converted into non-threshold recurrence plots. The VGG-16 convolutional neural network is used to extract the nonlinear features of the recurrence plot, and established a wrist pulse classification model automatically. The experimental results show that the classification accuracy of our method is 98.14%. The accuracy is higher than other existing wrist pulse classification methods. The research provides a new method for the classification of wrist pulse signal, and it is practical for wrist pulse diagnosis.

Key words: wrist pulse, non-threshold recurrence plot, convolutional neural network, nonlinear analysis