Application Of The Short-Time Fourier Transform And Lifting Wavelet Transform To The Analysis Of Human Pulse Signals

Traditional Chinese medicine all along receives publicity for its unique diagnostic method and particularly curative effects. With the development of sensor and computer technology, people hope to apply modern technology to human pulse diagnosis to reveal the essence and features of pulse phenomena scientifically, which is the main research aspect in this paper.Short-time Fourier transform (STFT) and Wavelet transform are most commonly used in the time-frequency representation of signals. This paper deduced the theorems and formulas of the Short-time Fourier transform and wavelet transform, and discussed the physics meaning of them.The basic idea of short-time Fourier transfom is to divide signal into many small time interval, using Fourier transform to analyse the each time interval in order to determine the spectrum information of the interval. In this paper,an efficient recursive algorithm with all-pole moving-windows is used to analyze the discrete short-time power spectra of pulse signals for 15 heroin addicts and 22 healthy persons.Then through extracting the average frequency and frequency centre of specified frequency region to analyse,it is found that the minimal average frequency and maximal frequency centre of heroin addicts is generally lower than that of healthy persons.Thus, as to minimal average frequency, 21 healthy persons and 14 heroin addicts are identified, Only one heroin addict B13 and two healthy persons Z05 and Z06 are misjudged, to maximal frequency center, 21 healthy persons and 15 heroin addicts are identified, only one healthy persons Z17 is misjudged. Finally using these two characteristic parameters as a two-dimensional characteristic vector, the critical parameter equation is determined that is used to classify heroin addicts and healthy persons.According to the equation, all healthy persons and heroin addicts are identified.Wavelet transform is a good analytical method both in the time and the frequency domains, especially applicable for non-stationary signal processing. Basing on customer wavelet transform, Lifting wavelet transform in this paper don't rely on Fourier transform and don't construct wavelet by expanding, contracting and using translation, but through a simple lifting method to construct wavelet. This paper also use db4 orthogonal wavelet conduct lifting wavelet transfom to analyses the 37 samples of pulse signals.By extract the eighth Component of the third layer of wavelet coefficients and the second Component of the third layer of scalar coefficients to construct two-dimensional, we found the significant difference between the heroin addicts and the healthy persons, a primary criterion for measuring off the heroin addicts and the healthy persons was obtained. Based on this criterion, Z01 were misjudged.Finally, we organize the two characteristic parameters from above method as a two-dimensional characteristic vector,adopts the the probabilistic neural network to identify the pulse signals.The experiments show the apply of network on human pulse has quick trained rate and the good clustery behavior,the discrimination reachs 97% and 100% respectively.