Decomposition of surface EMG signals
Decomposition of surface EMG signals
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DOI：
10.1152/jn.00009.2006
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发表时间：
2006-09-01
影响因子：
2.5
通讯作者：
Nawab, S. Hamid
中科院分区：
文献类型：
--
作者：
De Luca, Carlo J.;Adam, Alexander;Nawab, S. Hamid
这份报告描述了一个早期版本的技术分解成运动单位（MU）动作电位列车的组成表面肌电图（sEMG）信号。表面传感器阵列用于收集差分放大的肌电信号的四个通道。分解是通过一组算法来实现的，该算法使用专门开发的基于知识的人工智能框架。在自动模式下，精度范围为75%至91%。使用交互式编辑器将约30秒持续时间的信号历元的准确度提高到> 97%。通过比较表面传感器阵列和针传感器同时检测到的EMG信号的动作电位的发射来验证准确性。我们已经从检测到的眼轮匝肌，颈阔肌和胫骨前肌的表面肌电信号分解了多达六个MU动作电位列车。然而，产率通常较低，通常
This report describes an early version of a technique for decomposing surface electromyographic (sEMG) signals into the constituent motor unit ( MU) action potential trains. A surface sensor array is used to collect four channels of differentially amplified EMG signals. The decomposition is achieved by a set of algorithms that uses a specially developed knowledge-based Artificial Intelligence framework. In the automatic mode the accuracy ranges from 75 to 91%. An Interactive Editor is used to increase the accuracy to > 97% in signal epochs of about 30-s duration. The accuracy was verified by comparing the firings of action potentials from the EMG signals detected simultaneously by the surface sensor array and by a needle sensor. We have decomposed up to six MU action potential trains from the sEMG signal detected from the orbicularis oculi, platysma, and tibialis anterior muscles. However, the yield is generally low, with typically