Applied Neuromuscular Physiology Laboratory, Oklahoma State University, Stillwater, OK, USA.
School of Kinesiology, Tarleton State University, Stephenville, TX, USA.
Exp Brain Res. 2020 Nov;238(11):2475-2485. doi: 10.1007/s00221-020-05906-8. Epub 2020 Aug 25.
The purpose of this study was to determine if the implementation of a strict validation procedure, designed to limit the inclusion of inaccuracies from the decomposition of surface electromyographic (sEMG) signals, affects population-based motor unit (MU) analyses. Four sEMG signals were obtained from the vastus lateralis of 59 participants during isometric contractions at different relative intensities [30%, 70%, and 100% of maximal voluntary contraction (MVC)], and its individual motor unit potential trains (MUPTs) were extracted. The MUPTs were then excluded (ISIval) based on the coefficient of variation and histogram of the interspike intervals (ISI), the absence of additional clusters that reveals missed or additional firings, and more. MU population-based regression models (i.e., modeling the entire motor unit pool) were performed between motor unit potential size (MUP), mean firing rate (MFR), and recruitment threshold (RT%) separately for DSDC (includes all MUPTs without the additional validation performed) and ISIval data at each contraction intensity. The only significant difference in regression coefficients between DSDC and ISIval was for the intercepts of the MUP/MFR at 100% MVC. The validation had no other significant effect on any of the other regression coefficients for each of the contraction intensities. Our findings suggest that even though the decomposition of surface signals leads to some inaccuracies, these errors have limited effects on the regression models used to estimate the behavior of the whole pool. Therefore, we propose that motor unit population-based regression models may be robust enough to overcome decomposition-induced errors at the individual MU level.
本研究旨在确定严格验证程序的实施是否会影响基于人群的运动单位 (MU) 分析，该程序旨在限制表面肌电图 (sEMG) 信号分解中出现的不准确性。在不同相对强度（30%、70%和 100%最大自主收缩 (MVC)）下，从 59 名参与者的股外侧肌获得了四个 sEMG 信号，并提取了其个体运动单位潜在轨迹 (MUPT)。然后根据峰间间隔 (ISI) 的变异系数和直方图排除 MUPT（ISIval），排除缺失或额外放电的额外簇，以及更多。分别针对每个收缩强度的 DSDC（包括未经额外验证的所有 MUPT）和 ISIval 数据，对 MU 基于人群的回归模型（即对整个运动单位池进行建模）进行了运动单位潜在大小 (MUP)、平均放电率 (MFR) 和募集阈值 (RT%) 之间的回归分析。在 100% MVC 时，MUP/MFR 的截距是 DSDC 和 ISIval 之间唯一有显著差异的回归系数。验证对每个收缩强度的其他回归系数都没有其他显著影响。我们的发现表明，即使表面信号的分解会导致一些不准确，这些错误对用于估计整个池行为的回归模型的影响有限。因此，我们提出运动单位基于人群的回归模型可能足够稳健，可以克服个体 MU 水平上的分解引起的误差。
