van Dijk Johannes P, Blok Joleen H, Lapatki Bernd G, van Schaik Ivo N, Zwarts Machiel J, Stegeman Dick F
Department of Clinical Neurophysiology, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegan, The Netherlands.
Clin Neurophysiol. 2008 Jan;119(1):33-42. doi: 10.1016/j.clinph.2007.09.133. Epub 2007 Nov 26.
To present a motor unit number estimation (MUNE) technique that resolves alternation by means of high-density surface EMG.
High-density surface EMG, using 120 EMG channels simultaneously, is combined with elements of the increment counting technique (ICT) and the multiple-point stimulation technique. Alternation is a major drawback in the ICT. The spatial and temporal information provided by high-density surface EMG support identification and elimination of the effects of alternation. We determined the MUNE and its reproducibility in 14 healthy subjects, using a grid of 8 x 15 small electrodes on the thenar muscles.
Mean MUNE was 271+/-103 (retest: 290+/-109), with a coefficient of variation of 22% and an intra-class correlation of 0.88. On average, 22 motor unit potentials (MUPs) were collected per subject. The representativity of this MUP sample was quantitatively assessed using the spatiotemporal information provided by high-density recordings.
MUNE values are relatively high, because we were able to detect many small MUPs. Reproducibility was similar to that of other MUNE techniques.
Our technique allows collection of a large MUP sample non-invasively by resolving alternation to a large extent and provides insight into the representativity of this sample. The large sample size is expected to increase MUNE accuracy.
介绍一种通过高密度表面肌电图解决交替现象的运动单位数量估计(MUNE)技术。
同时使用120个肌电图通道的高密度表面肌电图与增量计数技术(ICT)和多点刺激技术的要素相结合。交替现象是ICT中的一个主要缺点。高密度表面肌电图提供的空间和时间信息有助于识别和消除交替现象的影响。我们在14名健康受试者的大鱼际肌上使用8×15个小电极的网格来确定MUNE及其可重复性。
平均MUNE为271±103(复测:290±109),变异系数为22%,组内相关性为0.88。每位受试者平均收集到22个运动单位电位(MUP)。使用高密度记录提供的时空信息对该MUP样本的代表性进行了定量评估。
MUNE值相对较高,因为我们能够检测到许多小的MUP。可重复性与其他MUNE技术相似。
我们的技术通过在很大程度上解决交替现象,能够无创地收集大量MUP样本,并深入了解该样本的代表性。大样本量有望提高MUNE的准确性。