Denoising method for surface myoelectricity signal based on CEEMD and improved wavelet threshold value

A technology of myoelectric signal and wavelet threshold, which is applied in the field of signal denoising and can solve problems such as signal pollution

Inactive Publication Date: 2019-08-23
HANGZHOU DIANZI UNIV
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But at the same time the signal i

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  • Denoising method for surface myoelectricity signal based on CEEMD and improved wavelet threshold value
  • Denoising method for surface myoelectricity signal based on CEEMD and improved wavelet threshold value
  • Denoising method for surface myoelectricity signal based on CEEMD and improved wavelet threshold value

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Embodiment Construction

[0059] The embodiments of the present invention are described in detail below in conjunction with the accompanying drawings: this embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection scope of the present invention is not limited to the following the described embodiment.

[0060] Such as figure 1 , figure 2 As shown, this embodiment includes the following steps:

[0061] Step 1, the collected surface EMG signal x(t) will contain noise n(t):

[0062] x(t)=s(t)+δn(t)

[0063] In the formula, s(t) is the SEMG signal without noise, x(t) is the SEMG signal with noise, n(t) is white noise, and δ is the scale factor of the noise.

[0064] Step 2, perform complementary set empirical mode decomposition on the noisy signal x(t) obtained in step 1, the specific steps are as follows:

[0065] ①The noisy signal x(t) is added with a pair of com...

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Abstract

The invention relates to a denoising method for a surface myoelectricity signal based on CEEMD and an improved wavelet threshold value. The method comprises the steps that complete ensemble empiricalmode decomposition is used for decomposing the surface myoelectricity signal to obtain intrinsic mode function components; then, through component correlation analysis, the proper intrinsic mode function components are selected, and all the selected intrinsic mode function components are subjected to improved wavelet threshold value processing; finally, the signal is subjected to signal reconstruction through the intrinsic mode function components processed through the improved wavelet threshold value and the intrinsic mode function components not processed through the improved wavelet threshold value, and a denoised signal is obtained. The method has self-adaptivity in signal processing and is suitable for analysis on the nonlinear and non-stable surface myoelectricity signal, the adverseeffect caused by mode aliasing can be reduced, useful information in the signal is reserved as much as possible, the influence caused by the noise is reduced, and it is proved through an experiment that compared with other denoising methods, the denoising method for the myoelectricity signal has a better effect.

Description

technical field [0001] The invention belongs to the field of signal denoising, and relates to a surface electromyography signal denoising method based on CEEMD and improved wavelet threshold. Background technique [0002] Surface electromyography (sEMG) is a weak bioelectric signal collected by the collection electrode. This bioelectric signal can reflect the relevant information of muscles and human behavior, and has been widely used in sports medicine and rehabilitation. areas such as training and machine control. The surface EMG signal is mainly distributed between 10Hz-500Hz, its amplitude is only 1μV, and it also has nonlinear and non-stationary characteristics. Therefore, EMG signals are easily polluted by noise. There are three main sources of noise: power line interference, white Gaussian noise, and baseline drift. Therefore, maintaining the purity of the signal is a prerequisite for the analysis and application of surface EMG signals. [0003] Currently, the com...

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Application Information

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IPC IPC(8): A61B5/0488
CPCA61B5/7203A61B5/389
Inventor 杨晨石鹏袁长敏章燕
Owner HANGZHOU DIANZI UNIV
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