AbdelMaseeh Meena, Stashuk Daniel W
IEEE Trans Neural Syst Rehabil Eng. 2017 Jul;25(7):1018-1025. doi: 10.1109/TNSRE.2017.2666741. Epub 2017 Feb 9.
A new measure of neuromuscular transmission instability, motor unit potential (MUP) jitter, is introduced. MUP jitter can be estimated quickly using MUP trains (MUPTs) extracted from electromyographic (EMG) signals acquired using conventional clinical equipment and needle EMG electrodiagnostic protocols. The primary motivation for developing MUP jitter is to avoid the technical demands associated with estimating jitter using conventional single fiber EMG techniques. At the core of the MUP jitter measure is a classifier capable of labeling a set of aligned MUP segments as single fiber MUP segments, i.e., parts of MUPs generated predominantly by a single fiber and not significantly contaminated by contributions from other fibers. For a set of MUPs generated by the same MU, these segments will have varying occurrence times within the MUPs, but will have consistent morphology across the MUPs. Pairs of sets of single fiber MUP segments generated by different fibers of the same MU and tracked across a MUPT can be used to estimate neuromuscular transmission instability. Aligning MUP segments is achieved using dynamic time warping. Results based on 680 simulated MUPTs show that MUP jitter can be estimated with an average error rate as low as 8.9%. Also, one or more sets of single fiber MUP segments can be detected in 85.3% of the studied trains. The analysis for a single MUPT can be completed in 3.6 s on average using a conventional personal computer.
引入了一种新的神经肌肉传递不稳定性测量方法——运动单位电位(MUP)抖动。使用从采用传统临床设备和针电极肌电图诊断协议采集的肌电图(EMG)信号中提取的运动单位电位序列(MUPTs),可以快速估计MUP抖动。开发MUP抖动的主要动机是避免使用传统单纤维肌电图技术估计抖动时的技术要求。MUP抖动测量的核心是一个分类器,它能够将一组对齐的MUP段标记为单纤维MUP段,即主要由单根纤维产生且未被其他纤维的贡献显著污染的MUP部分。对于由同一运动单位(MU)产生的一组MUP,这些段在MUP内的出现时间会有所不同,但在整个MUP中具有一致的形态。由同一MU的不同纤维产生并在一个MUPT中跟踪的成对单纤维MUP段集可用于估计神经肌肉传递不稳定性。使用动态时间规整实现MUP段的对齐。基于680个模拟MUPTs的结果表明,MUP抖动的估计平均错误率可低至8.9%。此外,在85.3%的研究序列中可以检测到一组或多组单纤维MUP段。使用传统个人计算机,对单个MUPT的分析平均可在3.6秒内完成。