Optimal Wavelet Filter for De-noising Surface Electromyographic Signal Captured From Biceps Brachii Muscle


This paper presents a study on finding an optimal wavelet filter for denoTsing surface electromyography signal, the surface electromyography signal was captured from the biceps brachii muscle of the human arm, This signal was stored in a one-dimensional matrix and conducted a series of procedures to reduce the noise. The performance has been tested based upon the nearest five wavelet filters in terms of the shape of the form of the original signal, after subjected to three noisy Gaussian environments at different signal to noise ratio. A tremendous amount of results was obtained, These results show that the fourth order Daubechies wavelet filter at the fourth decomposition level is optimized to reduce the noise of the surface electromyography signal that captured from biceps brachii muscle, where the results of the tests in a very noisy environment show that the value of the mean square error is 0.0159 and the output signal-to-noise ratio is 11.4424.