Method for analyzing instantly weighted synchronous extrusion wavelet bispectrum

Abstract

A method for analyzing instantly weighted synchronous extrusion wavelet bispectrum, comprising six steps as follows: step 1, determining minimum segmentation number of a signal, step 2, respectively calculating weight coefficient corresponded by each segmentation, step 3, forming a weight matrix of the signal with the weight coefficient corresponded by each signal segmentation and calculating the frequency sequence corresponded by the weight coefficient in each list, step 4, calculating synchronous extrusion wavelet coefficient of the signal and obtaining a time-frequency domain expression whose distribution is more compact, step 5, weighting the synchronous extrusion wavelet transformation coefficient according to an frequency interval to obtain a corrected wavelet coefficient, step 6, calculating weighted synchronous extrusion wavelet bispectrum and instantly weighted synchronous extrusion wavelet bispectrum. The method of the invention can be used for accurately differentiating the frequency component of the signal by using the synchronous extrusion wavelet transformation with higher frequency resolution and adding weight to all calculated synchronous extrusion wavelet coefficient via slicing according to the time in accordance with the information of the total signal frequency distribution, which overcomes the shortcomings that the traditional method is low in accuracy.

Description

technical field [0001] The invention belongs to the technical field of biomedical signal processing, in particular to an instantaneous weighted synchronous squeezing wavelet bispectral analysis method for detecting the instantaneous phase coupling relationship between non-stationary signals. Background technique [0002] Bispectrum analysis is an effective technique to quantitatively study the non-normal characteristics of nonlinear system response, and is widely used in econometrics, underwater acoustic signal processing, mechanical fault diagnosis and biomedical signal feature extraction and other fields. In the field of biomedical signal processing, since almost all physiological systems exhibit significant nonlinear behavior, the physiological signals generated by them have significant non-stationary and non-normal characteristics. This type of signal is mostly generated by a phase-coupled harmonic process, so bispectral analysis is often used in the analysis and process...

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