Turkish Journal of Electrical Engineering and Computer Sciences
Abstract
Denoising of surface electromyography (sEMG) signals plays a vital role in sEMG-based mechatronics applications and diagnosis of muscular diseases. In this study, 3 different denoising methods of sEMG signals, empirical mode decomposition, discrete wavelet transform (DWT), and median filter, are examined. These methods are applied to 5 different levels of noise-added synthetic sEMG signals. For the DWT-based denoising technique, 40 different wavelet functions, 4 different threshold-selection-rules, and 2 threshold-methods are tested iteratively. Three different window-sized median filters are applied as well. The SNR values of denoised synthetic signals are calculated, and the results are used to select DWT and median filter method parameters. Finally, 3 methods with the optimum parameters are applied to the real sEMG signal acquired from the flexor carpi radialis muscle and the visual results are presented.
DOI
10.3906/elk-1210-4
Keywords
Surface electromyography, sEMG, empirical mode decomposition, empirical mode decomposition, denoising, wavelet, median filter
First Page
931
Last Page
944
Recommended Citation
BAŞPINAR, ULVİ; ŞENYÜREK, VOLKAN YUSUF; DOĞAN, BARIŞ; and VAROL, HÜSEYİN SELÇUK
(2015)
"A comparative study of denoising sEMG signals,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 23:
No.
4, Article 1.
https://doi.org/10.3906/elk-1210-4
Available at:
https://journals.tubitak.gov.tr/elektrik/vol23/iss4/1
Included in
Computer Engineering Commons, Computer Sciences Commons, Electrical and Computer Engineering Commons