Wavelet denoising and evaluation of electromyogram signal using statistical algorithm Online publication date: Sat, 25-Apr-2015
by Karan Veer; Ravinder Agarwal
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 16, No. 4, 2014
Abstract: In this study, wavelet analysis has been exercised to understand the quality of surface electromyogram signal for class separability. The surface electromyogram signals were estimated with the following steps. First, the obtained signal was decomposed using wavelet transform. The decomposed coefficients were then analysed with threshold methods. With the appropriate choice of wavelet, it is possible to remove interference noise effectively in order to analyse the signal. This paper presents a comparative study of different Daubechies wavelets (db2-db14) family for analysis of arm motions. From the analysed results, it was inferred that wavelet db4 performs denoising the best among the wavelets and is suitable for accurate classification of surface electromyogram signal. Further, one-way repeated factorial Analysis of Variance (ANOVA) statistical technique was also implemented to investigate the voluntary muscular contraction relationship for different arm movements.
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