Surface Electromyogram Denoising Using Adaptive Wavelet Thresholding_Journal of Biomedical Engineering

Authors：

LOUZhi ^1,2 , DENGHao ¹ , CHENXiang ¹ , YAOBo ¹ ,  YANGJihai ¹

1. Department of Electronic Science & Technology, University of Science & Technology of China, Hefei 230027, China;
2. Department of Urban Rail Transit & Information Engineering, Anhui Communications Vocational & Technical College, Hefei 230051, China;

Corresponding author：

YANGJihai, Email: jhyang@ustc.edu.cn

Keywords：

surface electromyography; denoising using wavelet technique; adaptive threshold; decomposition of surface electromyogram signal

DOI：

10.7507/1001-5515.20140135

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Surface electromyogram (sEMG) may have low signal to noise ratios. An adaptive wavelet thresholding technique was developed in this study to remove noise contamination from sEMG signals. Compared with conventional wavelet thresholding methods, the adaptive approach can adjust thresholds based on different signal to noise ratios of the processed signal, thus effectively removing noise contamination and reducing distortion of the EMG signal. The advantage of the developed adaptive thresholding method was demonstrated using simulated and experimental sEMG recordings.

Citation： LOUZhi, DENGHao, CHENXiang, YAOBo, YANGJihai. Surface Electromyogram Denoising Using Adaptive Wavelet Thresholding. Journal of Biomedical Engineering, 2014, 31(4): 723-728. doi: 10.7507/1001-5515.20140135 Copy

1.	CLANCY E A, MORIN E L, MERLETTI R. Sampling, noise-reduction and amplitude estimation issues in surface electromyography[J]. J Electromyogr Kinesiol, 2002, 12(1): 1-16.
2.	RAEZ M B, HUSSAIN M S, MOHD-YASIN F. Techniques of EMG signal analysis: detection, processing, classification and applications[J]. Biol Proced Online, 2006, 8(1): 11-35.
3.	潘泉,张磊,孟晋丽,等.小波滤波方法及应用[M].北京:清华大学出版社,2005.
4.	XU Y, WEAVER J B, HEALY D M, et al. Wavelet transform domain filters: a spatially selective noise filtration technique[J]. IEEE Trans Image Process, 1994, 3(6): 747-758.
5.	MALLAT S, HWANG W L. Singularity detection and processing with wavelets[J]. IEEE Trans Inf Theo, 1992, 38(2): 617-643.
6.	DONOHO D L. De-noising by soft-thresholding[J]. IEEE Trans Inf Theo, 1995, 41(3): 613-627.
7.	DONOHO D L, JOHNSTONE J M. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika, 1994, 81(3): 425-455.
8.	张磊,潘泉,张洪才,等.小波域滤波阈值参数c的选取[J].电子学报,2001,29(3):400-402.
9.	李强.表面肌电信号的运动单位动作电位检测[D].合肥:中国科技大学,2008.
10.	雷培源,杨基海,陈香.基于三级滤波器的表面肌电信号降噪处理[J].北京生物医学工程,2011,30(1):62-66.
11.	赵章琰.表面肌电信号检测和处理中若干关键技术研究[D].合肥:中国科学技术大学,2010.
12.	赵章琰,陈香,成娟,等. 表面肌电图系统及其电极板:中国ZL200910136532.7[P].2011-04.

1. CLANCY E A, MORIN E L, MERLETTI R. Sampling, noise-reduction and amplitude estimation issues in surface electromyography[J]. J Electromyogr Kinesiol, 2002, 12(1): 1-16.
2. RAEZ M B, HUSSAIN M S, MOHD-YASIN F. Techniques of EMG signal analysis: detection, processing, classification and applications[J]. Biol Proced Online, 2006, 8(1): 11-35.
3. 潘泉,张磊,孟晋丽,等.小波滤波方法及应用[M].北京:清华大学出版社,2005.
4. XU Y, WEAVER J B, HEALY D M, et al. Wavelet transform domain filters: a spatially selective noise filtration technique[J]. IEEE Trans Image Process, 1994, 3(6): 747-758.
5. MALLAT S, HWANG W L. Singularity detection and processing with wavelets[J]. IEEE Trans Inf Theo, 1992, 38(2): 617-643.
6. DONOHO D L. De-noising by soft-thresholding[J]. IEEE Trans Inf Theo, 1995, 41(3): 613-627.
7. DONOHO D L, JOHNSTONE J M. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika, 1994, 81(3): 425-455.
8. 张磊,潘泉,张洪才,等.小波域滤波阈值参数c的选取[J].电子学报,2001,29(3):400-402.
9. 李强.表面肌电信号的运动单位动作电位检测[D].合肥:中国科技大学,2008.
10. 雷培源,杨基海,陈香.基于三级滤波器的表面肌电信号降噪处理[J].北京生物医学工程,2011,30(1):62-66.
11. 赵章琰.表面肌电信号检测和处理中若干关键技术研究[D].合肥:中国科学技术大学,2010.
12. 赵章琰,陈香,成娟,等. 表面肌电图系统及其电极板:中国ZL200910136532.7[P].2011-04.

Next Article
Non-contact Heart Rate Estimation Based on Joint Approximate Diagonalization of Eigenmatrices Algorithm

Journal of Biomedical Engineering

Surface Electromyogram Denoising Using Adaptive Wavelet Thresholding

Abstract Full text Figures/Tables Video References Cited by

Next Article

Format

Content