Beck Rebecca B, O'Malley Mark J, Stegeman Dick F, Houtman Caroline J, Connolly Sean, Zwarts Machiel J
Department of Electronic and Electrical Engineering, University College Dublin, Dublin, Ireland.
Muscle Nerve. 2005 Oct;32(4):506-14. doi: 10.1002/mus.20375.
New surface electromyogram (SEMG) techniques offer the potential to advance knowledge of healthy and diseased motor units. Conduction velocity (CV) estimates, obtained from indwelling electrodes, may provide diagnostic information, but the standard method of CV estimation from SEMG may be of only limited value. We developed a motor unit (MU) tracking algorithm to extract motor unit conduction velocity (MUCV) and motor unit action potential (MUAP) amplitude estimates from SEMG. The technique is designed to provide a noninvasive means of accessing fatigue and recruitment behavior of individual MUs. We have applied this MU tracking algorithm to SEMG data recorded during isometric fatiguing contractions of the tibialis anterior (TA) muscle in nine healthy subjects, at 30%-40% maximum voluntary contraction (MVC). The results reveal that MUCVs and MUAP amplitudes of individual MUs can be estimated and tracked across time. Time-related changes in the MU population may also be monitored. Thus, the SEMG technique employed provides insight into the behavior of the underlying muscle at the MU level by noninvasive means.
新的表面肌电图(SEMG)技术为增进对健康和患病运动单位的了解提供了潜力。从植入电极获得的传导速度(CV)估计值可能提供诊断信息,但从SEMG估计CV的标准方法可能价值有限。我们开发了一种运动单位(MU)跟踪算法,以从SEMG中提取运动单位传导速度(MUCV)和运动单位动作电位(MUAP)幅度估计值。该技术旨在提供一种非侵入性方法来了解单个运动单位的疲劳和募集行为。我们已将此MU跟踪算法应用于9名健康受试者在30%-40%最大自主收缩(MVC)时胫骨前肌(TA)等长疲劳收缩期间记录的SEMG数据。结果表明,单个运动单位的MUCV和MUAP幅度可以随时间进行估计和跟踪。运动单位群体随时间的变化也可以被监测。因此,所采用的SEMG技术通过非侵入性手段深入了解了基础肌肉在运动单位水平的行为。