Publication: A comparative study of denoising sEMG signals
Abstract
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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.
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.