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在表面肌电信号分解中使用二维空间信息。

Using two-dimensional spatial information in decomposition of surface EMG signals.

作者信息

Kleine Bert U, van Dijk Johannes P, Lapatki Bernd G, Zwarts Machiel J, Stegeman Dick F

机构信息

Department of Clinical Neurophysiology, Institute of Neurology, Radboud University Nijmegen Medical Center, PO Box 9101, 6500HB Nijmegen, The Netherlands.

出版信息

J Electromyogr Kinesiol. 2007 Oct;17(5):535-48. doi: 10.1016/j.jelekin.2006.05.003. Epub 2006 Aug 10.

Abstract

Recently, high-density surface EMG electrode grids and multi-channel amplifiers became available for non-invasive recording of human motor units (MUs). We present a way to decompose surface EMG signals into MU firing patterns, whereby we concentrate on the importance of two-dimensional spatial differences between the MU action potentials (MUAPs). Our method is exemplified with high-density EMG data from the vastus lateralis muscle of a single subject. Bipolar and Laplacian spatial filtering was applied to the monopolar raw signals. From the single recording in this subject six different simultaneously active MUs could be distinguished using the spatial differences between MUAPs in the direction perpendicular to the muscle fiber direction. After spike-triggered averaging, 125-channel two-dimensional MUAP templates were obtained. Template-matching allowed tracking of all MU firings. The impact of spatial information was measured by using subsets of the MUAP templates, either in parallel or perpendicular to the muscle fiber direction. The use of one-dimensional spatial information perpendicular to the muscle fiber direction was superior to the use of a linear array electrode in the longitudinal direction. However, to detect the firing events of the MUs with a high accuracy, as needed for instance for estimation of firing synchrony, two-dimensional information from the complete grid electrode appears essential.

摘要

最近,高密度表面肌电图电极网格和多通道放大器可用于非侵入性记录人体运动单位(MU)。我们提出了一种将表面肌电图信号分解为运动单位放电模式的方法,在此过程中我们着重关注运动单位动作电位(MUAP)之间二维空间差异的重要性。我们的方法以来自一名受试者股外侧肌的高密度肌电图数据为例进行说明。对单极原始信号应用双极和拉普拉斯空间滤波。通过使用在垂直于肌纤维方向上的MUAP之间的空间差异,从该受试者的单次记录中可以区分出六个不同的同时活跃的运动单位。经过触发尖峰平均后,获得了125通道二维MUAP模板。模板匹配允许跟踪所有运动单位的放电。通过使用在平行或垂直于肌纤维方向上的MUAP模板子集来测量空间信息的影响。使用垂直于肌纤维方向的一维空间信息优于使用纵向线性阵列电极。然而,要像例如估计放电同步性所需要的那样高精度地检测运动单位的放电事件,来自完整网格电极的二维信息似乎至关重要。

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