SEMG Signal Processing and Analysis Using Wavelet Transform and Higher Order Statistics to Characterize Muscle Force

An algorithm is proposed for processing and analyzing surface electromyography (SEMG) signals using wavelet transform and Higher Order Statistics (HOS). EMG signal acquires noise while travelling though different media. Wavelet denoising is performed in this research for initial EMG signal processing. With the appropriate choice of the Wavelet Function (WF), it is possible to remove interference noise effectively. Root Mean Square (RMS) difference and Signal to Noise Ratio (SNR) values are calculated to determine the most suitable WF. Results show that WF db2 performs denoising best among the other wavelets. Power spectrum analysis is performed to the denoised SEMG to indicate changes in muscle contraction. Furthermore, HOS method is applied for further efficient processing due to the unique properties of HOS applied to random time series. Gaussianity and linearity tests are conducted as part of HOS which shows that SEMG signal becomes less gaussian and more linear with increased force. Key-Words: -SEMG, wavelet transform, denoising, mean power frequency, HOS, bispectrum.