EMG Signal Processing and Application Based on Empirical Mode Decomposition

Xu Mengying; Yang Xiaoli; Xu Chenli; Yang Bin

doi:doi:10.11648/j.mcs.20190406.11

| Peer-Reviewed

EMG Signal Processing and Application Based on Empirical Mode Decomposition

Xu Mengying, Yang Xiaoli, Xu Chenli, Yang Bin

Published in Mathematics and Computer Science (Volume 4, Issue 6)

Received: 10 October 2019 Accepted: 22 November 2019 Published: 6 December 2019

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

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.

Published in	Mathematics and Computer Science (Volume 4, Issue 6)
DOI	10.11648/j.mcs.20190406.11
Page(s)	99-103
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

EMG, Wavelet Transform, EMD

References

[1]	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).
[2]	Feng Hongwu, Wang Jianchang. Application Research of Hilbert-Huang Transform in Time-Frequency Analysis of Seismic Signals. Plateau Earthquake Research, 30 (2018).
[3]	Ma xin, Hao yanan. Research on the denoising method of empirical mode decomposition. Science and technology vision, 23 (2018).
[4]	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).
[5]	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).
[6]	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).
[7]	Hao Liu, Jun Tao, Pan Lyu. Human-robot cooperative control based on sEMG for the upper limb exoskeleton robot, (2019).
[8]	Hu aijun, Ssun jingjing, Xiang ling. Modal aliasing in empirical modal decomposition. Vibration, testing and diagnosis, 31 (2011).
[9]	Xiao Ying, DONG Yu hua. Adaptive Noise Cancellation Method Based on Empirical Mode Decomposition. Journal of Dalian Minzu University, 21 (2019).
[10]	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).
[11]	Jia yutao, Luo zhizeng. Review on the methods of electromyographic signal feature extraction. Electronic devices, 30 (2007).
[12]	Norden E, Huang Zheng Shen, Steven R. Long. A new view of nonlinear water waves: The Hilbert Spectrum, 1999, 31, 417-457.
[13]	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.
[14]	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.
[15]	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.

Cite This Article

Plain Text BibTeX RIS

APA Style

Xu Mengying, Yang Xiaoli, Xu Chenli, Yang Bin. (2019). EMG Signal Processing and Application Based on Empirical Mode Decomposition. Mathematics and Computer Science, 4(6), 99-103. https://doi.org/10.11648/j.mcs.20190406.11

Copy | Download

ACS Style

Xu Mengying; Yang Xiaoli; Xu Chenli; Yang Bin. EMG Signal Processing and Application Based on Empirical Mode Decomposition. Math. Comput. Sci. 2019, 4(6), 99-103. doi: 10.11648/j.mcs.20190406.11

Copy | Download

AMA Style

Xu Mengying, Yang Xiaoli, Xu Chenli, Yang Bin. EMG Signal Processing and Application Based on Empirical Mode Decomposition. Math Comput Sci. 2019;4(6):99-103. doi: 10.11648/j.mcs.20190406.11

Copy | Download

@article{10.11648/j.mcs.20190406.11,
  author = {Xu Mengying and Yang Xiaoli and Xu Chenli and Yang Bin},
  title = {EMG Signal Processing and Application Based on Empirical Mode Decomposition},
  journal = {Mathematics and Computer Science},
  volume = {4},
  number = {6},
  pages = {99-103},
  doi = {10.11648/j.mcs.20190406.11},
  url = {https://doi.org/10.11648/j.mcs.20190406.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20190406.11},
  abstract = {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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - EMG Signal Processing and Application Based on Empirical Mode Decomposition
AU  - Xu Mengying
AU  - Yang Xiaoli
AU  - Xu Chenli
AU  - Yang Bin
Y1  - 2019/12/06
PY  - 2019
N1  - https://doi.org/10.11648/j.mcs.20190406.11
DO  - 10.11648/j.mcs.20190406.11
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 99
EP  - 103
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20190406.11
AB  - 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.
VL  - 4
IS  - 6
ER  -

Copy | Download

Author Information

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

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Xu Mengying, Yang Xiaoli, Xu Chenli, Yang Bin. (2019). EMG Signal Processing and Application Based on Empirical Mode Decomposition. Mathematics and Computer Science, 4(6), 99-103. https://doi.org/10.11648/j.mcs.20190406.11

Copy | Download

ACS Style

Xu Mengying; Yang Xiaoli; Xu Chenli; Yang Bin. EMG Signal Processing and Application Based on Empirical Mode Decomposition. Math. Comput. Sci. 2019, 4(6), 99-103. doi: 10.11648/j.mcs.20190406.11

Copy | Download

AMA Style

Xu Mengying, Yang Xiaoli, Xu Chenli, Yang Bin. EMG Signal Processing and Application Based on Empirical Mode Decomposition. Math Comput Sci. 2019;4(6):99-103. doi: 10.11648/j.mcs.20190406.11

Copy | Download

@article{10.11648/j.mcs.20190406.11,
  author = {Xu Mengying and Yang Xiaoli and Xu Chenli and Yang Bin},
  title = {EMG Signal Processing and Application Based on Empirical Mode Decomposition},
  journal = {Mathematics and Computer Science},
  volume = {4},
  number = {6},
  pages = {99-103},
  doi = {10.11648/j.mcs.20190406.11},
  url = {https://doi.org/10.11648/j.mcs.20190406.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20190406.11},
  abstract = {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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - EMG Signal Processing and Application Based on Empirical Mode Decomposition
AU  - Xu Mengying
AU  - Yang Xiaoli
AU  - Xu Chenli
AU  - Yang Bin
Y1  - 2019/12/06
PY  - 2019
N1  - https://doi.org/10.11648/j.mcs.20190406.11
DO  - 10.11648/j.mcs.20190406.11
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 99
EP  - 103
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20190406.11
AB  - 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.
VL  - 4
IS  - 6
ER  -

Copy | Download