Pulse signal classification method based on wavelet packet conversion and hidden markov models

Abstract

The invention discloses a pulse signal classification method based on wavelet packet conversion and hidden markov models. The method includes the following steps that a db4 wavelet is adopted as a wavelet basis function of wavelet packet conversion, and the wavelet packet conversion is carried out on two kinds of collected pulse signals to obtain wavelet packet decomposition coefficients of various frequency bands; an optimal frequency band is selected according to a local area discriminant base algorithm; an optimal energy feature vector is selected by means of a Fisher criterion; one part of the two kinds of pulse signals is selected to serve as training signals, the other part of the two kinds of the pulse signals serves as testing signals, and the optimal feature vectors of the two kinds of signals are figured out according to the method; the optimal energy feature vector of the training signals serves as a continuous hidden markov observation vector to train two hidden markov models; the optimal energy feature vector of the testing signals is respectively input into the trained two models, the probability values P(O | lambada i) of the optimal energy feature vectors are worked out according to a forward-backward algorithm, the probability values are compared, and classification of the pulse signals is completed.

Description

technical field [0001] The invention relates to a pulse signal classification method based on wavelet packet transform and hidden Markov model. Background technique [0002] A pulse is an oscillation of the blood and vessel walls caused by the ejecting activity of the heart. This oscillating wave is initially formed at the root of the aorta, and then rapidly propagates along the arterial tree to the peripheral blood vessels, becoming the performance wave of each part of the pulse. In the traditional study of pulse conditions, the pulse is distinguished by the feeling of the fingers under different fingerings, and the use of vivid natural scenes or imaginary schematic diagrams to describe the pulse conditions lacks clear physical meanings, and it is inevitable for everyone to feel under the fingers. There are differences, and this pulse identification method cannot establish a unified objective standard. In order to complete the quantitative and qualitative objective resear...

AI Technical Summary

Problems solved by technology

At present, wavelet transform is widely used in pulse signal processing. However, how to choose the optimal wavelet packet decomposition algorithm suitable for pulse signal feature extraction has not been solved, and it is difficult to classify different pulse signals.

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