Feature Extraction And Recognition Of Traditonal Chinese Medicine Auculatation Based On Nonlinear Dynamics

There are some limitations on the speech signal processing based on different traditional segmentation method, conserdering the mechanism of speech, speech signal is a kind of nonstationary and random process, and the characteristics of it is changing as the change of time. And the traditional speech signal processing and identifying technology based on linear system theories are difficult to develop. Firstly non-linear test using alternative method was done to test the auculatation signals, and the conclusion that there are nonlinearity in the signals was made. Based on this conclusion, two parameters Apen and SampEn which measure the complexity of the signals were used to analyzed the signals. Firstly the relationship between the frequency, amplitude, phase, signal-to-noise(SNR) and the value of Apen were analyzed using some typical signals. The combinaiton among the apen, sampen and wavelet packet analysis were proposed to analyze the signals. Then statistics for these feature were made, Finally SVM was chose to classify the samples, and a impressed results were obtained.Correlation dimension and the method of choosing suitable parameters were discussed. Firstly three segmentation of a typical signal were made to conclude that the attactor for the signal is a kind of strange attractor and there are chaos in the signals, then a suitable method was chose to get a appropriate parameter in order to calculate the correlation dimension for all the sample signals using G-P algorithm. Then the conclusion that it is easy classify the samples when the embedding dimension is higher.The theories and parameter choosing on recurrence plots(RP) and recurrence quantification analysis(RQA) were discussed. Then different Structure characteristics of RP were explained using some special RP drawings. Some suitable parameters of calculating the RQA parameters were chose using average mutual inforamtion and false nearest neighbor method. Based all these researches, all RQA parameters were calculated, and in these parameters, DET and L_max were discussed specifically. wavelet packet transform and apen, sampan were combined together to analyze the/a,//I,//u,//e,//o/signals of heart diseases patients, liver disease, lung department is disease patients, spleen and kidney disease patients, then make statistical analysis for the signals which is the basement for the following clasificaiton.Finally TCM ausculatation system was preliminarily realized based on VC++6.0, Users can collect real-time signal and open the existing wav files to do analysis. Users can extract linear and non-linear features in order to recognize what kind of symptoms they belong to. And the system realize the fuction of signal processing basically.