A method for editing motor unit potential trains obtained by decomposition of surface electromyographic signals. (February 2020)

• Record Type: Journal Article
• Authors: Kumar, Robert I.; Mallette, Matt M.; Cheung, Stephen S.; Stashuk, Daniel W.; Gabriel, David A.
• Is Part Of: Journal of electromyography and kinesiology. Volume 50(2020)
• Publication Date: 2020-02
• Subjects: Surface decomposition -- Validity -- Denoising algorithm -- Electromyography -- Error-filtered estimation algorithm -- Motor units; Electromyography -- Periodicals; Kinesiology -- Periodicals; Electromyography -- Periodicals; Movement -- physiology -- Periodicals; Muscles -- physiology -- Periodicals; Électromyographie -- Périodiques; Cinésiologie -- Périodiques; Electromyography; Kinesiology; Electronic journals; Periodicals; 616.740757
• DOI: 10.1016/j.jelekin.2019.102383
• Languages: English
• ISSNs: 1050-6411

Abstract

Abstract: Rather than discarding motor unit potential trains (MUPTs) because they do not meet 100% validity criteria, we describe and evaluate a novel editing routine that preserves valid discharge times, based on decreasing shape variability (variance ratio, VR) within a MUPT. The error filtered estimation (EFE) algorithm is then applied to the remaining 'high confidence' discharge times to estimate inter-discharge interval (IDI) statistics. Decomposed surface EMG data from the flexor carpi radialis recorded from 20 participants during 60% MVC wrist flexion was used. There were two levels of denoising criteria (relaxed and strict) criteria for removing MUPs to decrease the VR and increase the signal-to-noise ratio (SNR) of a MUPT. In total, VR decreased 24.88% and SNR increased 6.0% ( p 's < 0.05). The MUP template peak-to-peak (P-P) amplitude and P-P duration were dependent on the level of denoising ( p 's < 0.05). The standard error of the estimate (SEE) of the mean IDI before and after editing using the relaxed criteria (3.2% versus 3.69%), was very similar ( p > 0.05). The same was true for the SEE between denoising criteria, which increased only to 5.14% for the strict criteria ( p > 0.05). Editing the MUPTs resulted in a significant decrease in MUP shape variability and in the measures extracted from the MUP templates, with trivial differences between the SEE of the mean IDI between the edited and unedited MUPTs.