Complexity analysis method of non-linear EEG signals

A technology of complexity analysis and EEG signal, which is applied in the field of one-dimensional time-series EEG signal complexity analysis, can solve the problem of loss of sensitivity of results, achieve the effect of improving utilization rate, improving accuracy and reducing redundant information

Inactive Publication Date: 2017-12-01
BEIJING UNIV OF TECH
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Problems solved by technology

[0004] On the other hand, the sample entropy reconstruction component is compared with the threshold r with a tolerance linearly proportional to the standard deviation.

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  • Complexity analysis method of non-linear EEG signals
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  • Complexity analysis method of non-linear EEG signals

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Embodiment Construction

[0046] In this experiment, matlab2016 is used as a simulation tool, and Logistic mapping is used to simulate time series signals. Logistic mapping is a classic model for studying the behavior of complex systems such as dynamical systems, chaos, and fractals. It is a time-discrete dynamical system, which is iterated repeatedly according to the following equation :

[0047] x(t+1)=μx(t)(1-x(t))

[0048] Among them, t is the iterative time step. For any t, x(t)∈[0,1], μ is an adjustable parameter. In order to ensure that the mapped x(t) is always within [0,1], μ ∈[0,4]. When varying the parameter μ, the equation will exhibit different kinetic limit behaviors. When 0figure 1 shown:

[0049] With a sampling rate of 1000Hz, simulate a signal with 7000 signal points, and take the last 5000 as a stable signal. Firstly, each of the above time series signals is divided into symbolic processing, and then the phase space is reconstructed and the entropy value is calculated. The differ...

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Abstract

The invention discloses a complexity analysis method of non-linear EEG signals. Permutation entropy and sample entropy algorithms are greatly applied to the non-linear complexity analysis, but certain defects exist. The sample entropy is good in robustness and accuracy, but the calculation efficiency is not satisfactory; and the permutation entropy is rapid in calculation, but the calculation is not as accurate as that of the sample entropy. Aiming at the above problem, the method for the non-linear complexity analysis of the EEG signals includes: firstly performing filtering processing on the EEG signals, extracting effective frequency bands, ranking the frequency bands, performing equal-dividing symbolization assignment according to two rules, and finally respectively performing m dimensional and m+1 dimensional phase space construction for entropy calculation. According to the method, compared with the previous non-linear method, the accuracy of the permutation entropy is improved, and the calculation efficiency is improved for the sample entropy method.

Description

technical field [0001] The invention relates to the field of nonlinear signal analysis, in particular to a one-dimensional time-series EEG signal complexity analysis technology. Background technique [0002] EEG signals record and describe the electric wave changes of brain activity, which is the overall reflection of the electrical activity of brain nerve cells on the surface of the cerebral cortex or scalp. The traditional EEG analysis method mainly extracts the characteristics of EEG signal time domain and frequency domain. In recent years, studies have shown that the brain is a nonlinear dynamic system, and the corresponding EEG analysis has also shifted from the traditional time-frequency method to a nonlinear method. Therefore, related dimensions, Lyapunov exponents, wavelet entropy and complexity Nonlinear kinetic parameters are used in the study of EEG signals. Since EEG reflects the electrical activity of the brain with nonlinear system characteristics, it can pro...

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Application Information

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IPC IPC(8): G06K9/00A61B5/00
CPCA61B5/72G06F2218/08
Inventor 陈萌钟宁刘岩何强周海燕
Owner BEIJING UNIV OF TECH
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