Electromyography signal noise reducing and aliasing removing method based on second-generation wavelets and ICA (independent component analysis)

Abstract

The invention relates to an electromyography signal noise reducing and aliasing removing method based on combination of second-generation wavelet transform and ICA and aims to overcome some revealed defects when the current signal aliasing removing methods are applied to electromyography signals. The method includes: a second-generation wavelet noise reducing algorithm is first used to filter noises in the electromyography signals, the second-generation wavelet noise reducing algorithm is applied to the electromyography signals after noise reduction is smooth, unnecessary vibration in waveforms is restrained, and the electromyography signal features are evident; ICA separation is performed on the vague signals after noise reduction so as to fast remove the aliasing components in the signals. The method has the advantages that by the pre-treatment, interference in the signals can be removed greatly, and convenience is brought to subsequent researches such as electromyography signal feature extraction and action identification.

Description

technical field [0001] The invention belongs to the field of anti-aliasing of multi-channel signals, and relates to a noise elimination and anti-aliasing method applied to electromyographic signals. Background technique [0002] EMG signals contain a wealth of muscle movement information, which can reflect the movement patterns of limbs. At present, EMG signals have been widely used in clinical diagnosis, rehabilitation engineering, sports medicine and other fields. Surface electromyography (sEMG) is a bioelectrical signal that is recruited on the surface of the skin and produced by muscle contraction following limb movement. The human-machine interface based on EMG signals can identify different action patterns of the human body through surface EMG signal processing and pattern recognition, and control the work of external environmental equipment, such as the control of bionic prosthetic hands based on EMG signals. Fall recognition. [0003] In the research of human-comp...

AI Technical Summary

Problems solved by technology

[0006] The traditional wavelet denoising method decomposes the signal at multiple scales, and uses a wavelet basis function to approximate signals on different scales. The difference between the wavelet basis function and the approximate signal will generate detailed information, and these detailed signals will be processed by thresholding. It is filtered out as noise, so that the signal after noise reduction loses some useful information

In this way, the conventional wavelet noise reduction method cannot accurately describe the non-stationary signal

[0007] In summary, the existing signal denoising and anti-aliasing methods have shortcomings when applied to EMG signals

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