Correlation-based decomposition of surface electromyograms at low contraction forces
Correlation-based decomposition of surface electromyograms at low contraction forces
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DOI：
10.1007/bf02350989
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发表时间：
2004-07-01
影响因子：
3.2
通讯作者：
Zazula, D
中科院分区：
文献类型：
--
作者：
Holobar, A;Zazula, D
本文研究了一种表面肌电图（SEMG）分解技术，适用于识别低水平等长随意肌收缩过程中完整运动单位（MU）的放电模式及其运动单位动作电位（MUAP）。该算法是基于测量的相关矩阵，假设非同步（不相关）MU发射，表现出非常低的计算复杂度，并解决了MUAP的叠加。分离指数被定义为确定MU激活的时刻，并最终用于重建一个完整的MU神经支配脉冲串。与其他的分解技术相比，所提出的方法也工作得很好，当活跃MU的数量被稍微低估，如果MU发射模式部分重叠，如果测量是嘈杂的。合成表面肌电信号的结果表明，在检测神经支配脉冲的准确性下降到10 dB的信噪比（SNR），93 +/- 4.6%（平均标准偏差）的准确性与0 dB的加性噪声。在真实的表面肌电的情况下，记录与一个阵列的61个电极从肱二头肌的5名受试者在10%的最大自主收缩，7个活跃的MU的平均放电率为14.1 Hz的平均确定。
The paper studies a surface electromyogram (SEMG) decomposition technique suitable for identification of complete motor unit (MU) firing patterns and their motor unit action potentials (MUAPs) during low-level isometric voluntary muscle contractions. The algorithm was based on a correlation matrix of measurements, assumed unsynchronised (uncorrelated) MU firings, exhibited a very low computational complexity and resolved the superimposition of MUAPs. A separation index was defined that identified the time instants of an MU's activation and was eventually used for reconstruction of a complete MU innervation pulse train. In contrast with other decomposition techniques, the proposed approach worked well also when the number of active MU's was slightly underestimated, if the MU firing patterns partly overlapped and if the measurements were noisy. The results on synthetic SEMG show 100% accuracy in the detection of innervation pulses down to a signal-to-noise ratio (SNR) of 10dB, and 93 +/- 4.6% (mean standard deviation) accuracy with 0dB additive noise. In the case of real SEMG, recorded with an array of 61 electrodes from biceps brachii of five subjects at 10% maximum voluntary contraction, seven active MUs with a mean firing rate of 14.1 Hz were identified on average.