Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, the Netherlands.
Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, the Netherlands; Department of Neurology and Clinical Neurophysiology, Radboud University Medical Center, Nijmegen, the Netherlands.
J Electromyogr Kinesiol. 2022 Oct;66:102679. doi: 10.1016/j.jelekin.2022.102679. Epub 2022 Jul 3.
While a decreasing spectral content of surface electromyography reflects low back muscle fatigue development, reliability of these decreases may be insufficient. Decreasing frequency content is largely determined by decreasing average motor unit action potential conduction velocities (CV), which is considered a more direct measure of muscle fatigue development. However, for the low back muscles it has been proven difficult to identify propagating potentials and consequently estimate the CV. The aim of this study was to estimate the low back muscle CV from high-density multi-channel electromyography by using peak-delay and cross-correlation methods. Fourteen healthy male participants without a history of low-back pain performed a 30 degrees lumbar flexion trial until exhaustion while standing. For 10 out of the 14 participants (118 out of 560 sites) realistic CV estimates were obtained using both methods, the majority likely over the iliocostalis lumborum muscle. Between-method CV differences appeared to be small. Close to the spine a considerable number of sites (79) yielded systematically overestimated low back muscle CV values. Estimating low back muscle CV may allow additional insight into low back muscle fatigue development and potentially improve its monitoring using (high-density) surface electromyography.
虽然表面肌电图的频谱内容减少反映了腰背肌肉疲劳的发展，但这些减少的可靠性可能不足。频率内容的减少在很大程度上取决于运动单位动作电位传导速度（CV）的平均减少，这被认为是肌肉疲劳发展的更直接的衡量标准。然而，对于腰背肌肉，已经证明很难识别传播电位，因此难以估计 CV。本研究的目的是通过使用峰延迟和互相关方法，从高密度多通道肌电图中估计腰背肌肉的 CV。14 名没有腰背疼痛史的健康男性参与者在站立时进行 30 度腰椎屈曲试验，直到疲劳。对于 14 名参与者中的 10 名（560 个部位中的 118 个），两种方法都获得了现实的 CV 估计值，大多数可能在髂肋肌上。两种方法之间的 CV 差异似乎很小。靠近脊柱的部位（79 个）系统地产生了过高的腰背肌肉 CV 值。估计腰背肌肉 CV 可能有助于进一步了解腰背肌肉疲劳的发展，并可能通过（高密度）表面肌电图改善其监测。
