Complexity analysis method based on multi-scale window as threshold

Abstract

The invention discloses a complexity analysis method based on a multi-scale window as a threshold. The research of an electroencephalogram signal mainly focuses on a conventional analysis method for time frequency features in the past, and more and more researches adopt nonlinear methods in recent years. The first steps of most nonlinear methods all involve the coarse graining problem, and excessive coarse graining causes loss of effective information in the electroencephalogram signal. In order to solve the problem, the complexity analysis method based on the multi-scale window is proposed. The method comprises the steps of firstly performing filtration processing on the electroencephalogram signal; extracting an effective frequency band; obtaining a window with different scales; taking a mid-value in the window as the threshold; and obtaining a unique threshold for each signal point to perform binarization processing and coarse graining. According to the method, the effectiveness of performing coarse graining by taking a mean value as the threshold before is improved and the effective information in the signal can be correctly extracted.

Description

technical field [0001] The invention relates to the field of electroencephalogram signal analysis, and is suitable for complex signals containing different frequency domains and nonlinear research on signals. Background technique [0002] For the research of EEG signals, in the past, it mainly focused on the traditional analysis method of time-frequency characteristics. In recent years, more and more researches have adopted nonlinear methods. The nonlinear dynamic method has the characteristics of non-randomness, non-periodicity, and nonlinearity, and is very suitable for analyzing time-varying, unsteady, and complex nonlinear time-series signals. Since brain neural activity is an extremely complex dynamic process, it is easy to Affected by different thinking states and external environments, EEG signals have obvious random and nonlinear characteristics. It is a new approach to extract and analyze EEG features using nonlinear dynamics methods. [0003] Complexity is an ind...

AI Technical Summary

Problems solved by technology

[0004] The LZ complexity algorithm first performs binarization processing on a sequence signal. The traditional method uses the mean as the threshold, and the binarization greater than the mean is 1, and the binarization is less than 0, but the traditional binarization method saves one. Disadvantages: Dividing with the average value will make many details of the original signal sequence unable to reflect

Changes in the low-frequency amplitude are more likely to affect the LZ complexity value, that is, the traditional calculation method that takes the average of the overall signal as the threshold will ignore the information of the high-frequency signal

Images