Study on De-noise of Electromyographic (EMG) Signal

Xiaoli Yang, Chenli Xu, Haoran Guan, Bin Yang, Wanrong Wang, Mengying Xu

Abstract


With the development of rehabilitation medicine and kinematics, the study of Electromyographic (EMG) signal come into people’s sight. The information obtained by the surface Electromyographic (SEMG) signal can not only reflect the motion state of muscles and joints, but also judge the types of people's movements and so on, it’s one of the important indicators for studying human body. Based on the EMG as the research object with the detailed analysis to understand the EMG of time domain, frequency domain and SNR, etc. In this paper, the de-noising method based on empirical mode decomposition (EMD) of EMG de-noising processing, according to the distribution rule of the EMD decomposition after each order the intrinsic mode function (IMF) to determine the main position, the noise is to contain the IMF component to deal with the noise of main noise energy. In addition, by comparing with wavelet transform de-noising method of analysis, we try to joint wavelet transform and EMD to de-noise. Compare and calculate the three groups of de-noising SNR, wavelet threshold de-noising method to remove power frequency interference in signal-to-noise ratio increase is better, when combined the EMD decomposition and wavelet threshold method in removing Electrocardiograph interference when the signal-to-noise ratio to improve more.


DOI
10.12783/dtcse/iciti2018/29189

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