Study Of The Pluse Character Based On Artificial Neural Networks

Pulse-feeling is the most characteristic diagnostic methods in traditional Chinese medicine. Along with the development of science and technology, people hope to apply sensors and modern signal processing technology to human pulse diagnosis in order to carrying on an investigation in the objectivity of the Chinese medicine Pulse- feeling, Thus, impersonal research of the pulse-diagnosis has important meaning for inheriting and expanding of our country Chinese medicine.First, designing the system for collecting pulse signal is introduced. The signal collection system procedure is established by VB in this paper, and the VB has the stronger advantage in the sketch customer interface development, but the software of MATLAB is made use of when processing, analyzing and identifying the signal, so it introduced the commutating the data between MATLAB and VB. Second, making use of the spectral analysis method, carried on the spectrum analysis to the time domain pulse signal, and get the frequency chart curve. Then by the characteristic analysis of the frequency chart distilled the spectral feature. This paper represents the basic conceptions and theories of Bispectrum estimationin detail, and discusses the physical meaning of the Bispectrum. While using the indirect algorithm and parametric model algorithm to analyze pulse signals, this paper also derives, verifies and uses them..Lastly, applies the network of RBF in the classification for four kinds of pulse, signal, and compares with the difference between the spectral characteristic and the energy of on the different dimensions by wavelet transformation and the boxing dimension and the spectral characteristic as the neural network input. Though the training sample is limited in the text, the emulational result indicates: for some particular characteristic of the pulse signal, we make use of the neural network to identify pulse is a viable and effective, and it has obviously superiority compared with the conventional pattern recognition method.