Please enter verification code
EMG Signal Processing and Application Based on Empirical Mode Decomposition
Mathematics and Computer Science
Volume 4, Issue 6, November 2019, Pages: 99-103
Received: Oct. 10, 2019; Accepted: Nov. 22, 2019; Published: Dec. 6, 2019
Views 734      Downloads 214
Xu Mengying, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
Yang Xiaoli, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
Xu Chenli, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
Yang Bin, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
Article Tools
Follow on us
With the development of rehabilitation medicine and kinematics, the study of Electromyographic (EMG) signal come into people’s sight. The information obtained from the surface EMG signals can not only reflect the motion state of muscles and joints, but also judge people's motion type, which is one of the important indexes in the study of 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. The study of EMG signal denoising and feature extraction is of great value and significance in the field of medical diagnosis. Such as using sEMG signals to assess muscle status and determine postoperative recovery status. Empirical Mode Decomposition (EMD) based on hilbert-huang is a time frequency analysis method for non-linear and non-stationary signals like EMG signals, which has unique advantages and broad prospects in signal analysis and processing. In this paper, we used EMD to decompose signal which contain multiple frequency component into a series of inherent modal parameters, and then combine the method of EMD decomposition and wavelet transform to carry out denoising processing and feature extraction for EMG signals, which can effectively weaken the noise of surface EMG signals and reflect the essential characteristics of the original signal, and classify the damage of EMG signals by analyzing the characteristic values.
EMG, Wavelet Transform, EMD
To cite this article
Xu Mengying, Yang Xiaoli, Xu Chenli, Yang Bin, EMG Signal Processing and Application Based on Empirical Mode Decomposition, Mathematics and Computer Science. Vol. 4, No. 6, 2019, pp. 99-103. doi: 10.11648/j.mcs.20190406.11
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Huang N E, Shen Z, Long S, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non -stationary time series analysis. Proceedings of the Royal Society of London, 454: 903-995 (1998).
Feng Hongwu, Wang Jianchang. Application Research of Hilbert-Huang Transform in Time-Frequency Analysis of Seismic Signals. Plateau Earthquake Research, 30 (2018).
Ma xin, Hao yanan. Research on the denoising method of empirical mode decomposition. Science and technology vision, 23 (2018).
Xi xugang, Zhu haigang, Luo zhizeng. Surface emg signal denoising method based on EEMD and second-generation wavelet transform. Journal of sensing technology, 25 (2016).
Cao Yakun, Guo Weidong, Shi Chaoling. Clinical study of high frequency ultrasound and electromyography in diagnosis of carpal tunnel syndrome of median nerve, 39 (2018).
Monika Błaszczyszyn, Agnieszka Szczesna, Katarzyna Piechota. sEMG Activation of the Flexor Muscles in the Foot during Balance Tasks by Young and Older Women: A Pilot Study, 16 (2019).
Hao Liu, Jun Tao, Pan Lyu. Human-robot cooperative control based on sEMG for the upper limb exoskeleton robot, (2019).
Hu aijun, Ssun jingjing, Xiang ling. Modal aliasing in empirical modal decomposition. Vibration, testing and diagnosis, 31 (2011).
Xiao Ying, DONG Yu hua. Adaptive Noise Cancellation Method Based on Empirical Mode Decomposition. Journal of Dalian Minzu University, 21 (2019).
Liu jian, Zou renling, Zhang dongheng et al. Research and development trend of surface emg signal feature extraction methods. Advances in biomedical engineering, 36 (2015).
Jia yutao, Luo zhizeng. Review on the methods of electromyographic signal feature extraction. Electronic devices, 30 (2007).
Norden E, Huang Zheng Shen, Steven R. Long. A new view of nonlinear water waves: The Hilbert Spectrum, 1999, 31, 417-457.
Noeden E. Huang, Man-Li C. Wu, Steven R. Long, etc. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis, 2003, 459, 2317-2345.
Zhang Tao, Ding Biyun, Zhao Xin. A Feature Extraction Method of Defect Detection Using Improved Hilbert—Huang Transform, Journal of xi’an Jaotong university, (52) 2018.
Wang sixian, Zhang lei, Duan Xiaoyi, Cui qi, Gao xianweil. Correlation Power Analysis Attack Based on Hilbert-Huang Transform Filtering Pretreatment, Computer Engineering, (44) 2018.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186