A denoising algorithm for surface EMG decomposition
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The goal of the present thesis was to investigate a novel motor unit potential train (MUPT) editing routine, based on decreasing the variability in shape (variance ratio, VR) of the MUP ensemble. Decomposed sEMG data from 20 participants at 60% MVC of wrist flexion was used. There were two levels of denoising (relaxed and strict) criteria for removing discharge times associated with waveforms that did not decrease the VR and increase its signal-to-noise ratio (SNR) of the MUP ensemble. The peak-to-peak amplitude and the duration between the positive and negative peaks for the MUP template were dependent on the level of denoising (p’s <0.05). The error-filtered estimation (EFE) algorithm was used to calculate the inter-discharge interval (IDI) for the denoised MUPTs. In total, VR decreased 24.88% and the SNR increased 6.0% (p’s < 0.05). The standard error of estimate (3.2 versus 3.69%) in mean IDI before and after denoising using the relaxed criteria, was very similar (p>0.05). The same was true between denoising criteria (p>0.05). Editing the MUPT based on MUP shape resulted in significant differences in measures extracted from the MUP template, with trivial difference between the standard error of estimate for mean IDIs between the complete and denoised MUPTs.