Recognition Of Manifestition Of The Pulse Signal With Artificial Intelligence

Diagnosis based on pulse tracings plays an important part both in theory and in clinical traditional Chinese medicine. Signal processing and pattern recognition of Human Pulse are studied and discussed in this thesis on the basis of the research and development of AI recognition system for Human Pulse. The traditional mathematical analysis of pulse tracing graph is a time domain analysis that found part parameters defining traditional Chinese medicine pulse tracings and interpreted the relation between pulse tracings and disease of entrails. As for the periodicity of pulse tracing signals, frequency domain analysis has been used. Based on the characteristics of Human Pulse, Wavelet Transform (WT) is originally used to process and analysis it. Wavelet analysis has a good qualities both in time domain and frequency domain and is an ideal tool in analyzing unsteady signal,so it is used to detect the singularity of Human Pulse and extract the features of Human Pulse in time-domain. Human Pulse is characterized by a new feature, which is the energy of its wavelet transform in different scales. BP Neural Network is used to classify the Human Pulse between health and Cerebrovascular Disease (CVD) according to its spectra features and new features extracted on d6 by Wavelet Transform. The results show that different inputs of features will lead to different outcomes of pattern recognition. In spite of limited training samples, the method in this thesis is superior to traditional Pattern Recognition methods if choose suitable features to be input-cell such as features extracted by Wavelet Transform. A data processing system base on DSP is designed according to the new characteristics.