The Study On Recognition Of Sub-health From Pulse Wave

Sub-health, also called "the third state", is defined as a critical state between the health and diseases. It is a kind of physiological state that appears with vigor reducing, adaptive capacity failing in various degrees, although there isn't any identified disease in organism. Sub-health could be transformed to the health if the state is dealt with aptly, and, contrariwise, to illness. Now, sub-health has been badly endangering the health of the residents. However, the state can't be diagnosed by traditional medical equipment because all necessary physical and clinical indexes are tested negative. At present, the diagnosis of sub-health is lacking of inexpensive, handy, objective and quantitative method that can evaluate sub-health state effectively. Fatigue was the primary cause of sub-health. This dissertation theoretically demonstrates the feasibility to evaluate people's sub-healthy state by quantifying fatigue, which is based on pulse analysis. 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.According to the Chinese medicine understanding, sub-health is the initial state which the humors (qi, moisture, blood) are affected, and organ systems suffer from dysfunction. Human pulse contains a lot of useful information about what goes on inside the body. So sub-health could be evaluated by analyzing pulse wave.Based on the study status in quo, working method and working process of the pulse recognition has been introduced in this dissertation. Firstly, pulse collection system was built by using HK-2000C digital integrated pulse transducer, and data collection project was designed. Pulse waves of more than 60 voluntary undergraduates were measured from their left wrist. Then, 17 sub-healthy data and 13 healthy data were chosen for analysis. Secondly, wavelet analysis which has a good qualities both in time domain and frequency domain and is an ideal tool in analyzing unsteady signal, is used in pretreatment of human pulse and good result has been obtained. In addition, considering the origin, the mechanism and the framework of the pulse signals, peak value, peak frequency, center of gravity (cg), gravity frequency of power spectrum, AR model parameter, the value of SER and Renyi entropy were extracted. Moreover, we successfully use Linear Discriminant Analysis (LDA) to identify sub-health status from the pulse waves of 17 sub-healthy persons and 13 healthy persons. The recognition accuracy is up to 86.667% by using peak value and peak frequency of power spectrum as characteristics. Only two sub-healthy personsare misjudged. The recognition accuracy of 80% was attained by using eg and gravity frequency of power spectrum as characteristics. The accuracy of 76.7% by using AR model parameter and of 73.3% by using Renyi entropy was obtained yet. SER value, which is often used in pulse recognition, can't identify the sub-health state effectively. Then, LDA and Support Vector Machine (SVM) were compared. The preferable result has been obtained by using LDA. Using SVM also gained good result in recognition, but there still are some problems in selection of kernel parameters that usually select by experience. Finally, the software design of sub-health recognition system has been introduced.