The Analysis And Research Of Pulse Signal About The Sub-Healthstate Based On Higher-Order Statistics

Sub-health is defined as a critical state between the health and diseases. It is a kind of physiological state, namely the state of transition, that appears with vigor reducing, adaptive capacity failing in various degrees, although there isn't any identified disease in organism. According to the Chinese medicine understanding, sub-health is the initial state which the humors are affected, and organ systems suffer from dysfunction. Human pulse contains a lot of useful information about what goes on inside the body. Pulse-feeling is the most characteristic diagnostic methods in traditional Chinese medicine, and it is the best diagnosis method to diagnose the sub-healthy state, so sub-health could be evaluated by analyzing pulse wave which is the main motive in this paper. This dissertation theoretically demonstrates the feasibility to evaluate people's sub-healthy state, and according to the needing of the scientific research, a new method for identifying the sub-health state from pulse wave is presented in this paper.Considering the characteristic differences between the pulse signals of sub-healthy persons and healthy persons, this dissertation successfully use the conventional approach and the parametric approach to analyze the bispectrum estimation of pulse signals for 10 sub-healthy persons and 10 healthy persons to classify the pulse signals. According to the phase character map of bispectrum estimation of the pulse signals, it is found that the phase of healthy persons on a specified region is generally lower than that of sub-healthy persons. Using the average phase as characteristic parameter, a critical parameter is determined that is used to classify sub-healthy persons and healthy persons. In parametric bispectrum estimation, the date segments of pulse signals are identified the exponent number and estimater the parameter number. Then the residual time series algorithm and the q-slice are used to estimate the bispectrum of pulse signals. Using the average phase approach to analyze the bispectrum, it is found that this approache can give the well classified identify to healthy persons and sub-healthy persons. It is shown that at the aspect of extracting characteristic information of pulse signals, the conventional bispectrum estimation and parametric bispectrum estimation all can carry out the better classification of identification and have the higher discrimination.This paper expounded the basic conceptions of HOS. Meanwhile, the basic theories of bispectrum estimation are represented in detail in this paper, and discusses the physical meaning of the bispectrum. While using examples to verifies and uses the conventional model and the parametric model.