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Automatic event detection in surface EMG of rhythmically activated muscles

机译:有节奏激活肌肉表面EMG的自动事件检测

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Precise detection of discrete events in the surface electromyogram (EMG) like the phasic change in the activity pattern associated with the initiation of a rapid motor response is an important issue in the analysis of the human motor system. However, accurate detection is difficult when the muscle is already involved in a secondary motor task, and two superimposed activation patterns have to be separated. This paper describes a method which allows automatic detection of phasic events that arise during simultaneous execution of rhythmical and discrete motor tasks by the same muscle. Based on a nonlinear signal model, events are identified as characteristic changes in the variance of the original EMG signal by using a two-window scheme and the generalized likelihood ratio test. Both, the beginning as well as the end of epochs with prominent EMG activity related to the rhythmical movement and the onset of phasic activity indicating initiation of a discrete contraction can be detected. Problems arising from modelling the EMG by a sequence of independent Gaussian random variables modulated by a deterministic control pattern are discussed.
机译:精确地检测表面电象(EMG)中的离散事件(EMG),如与快速电动机响应开始相关的活动模式的相位变化是人机系统分析中的一个重要问题。然而,当肌肉已经涉及二次电动机任务时,难以精确的检测,并且必须分离两个叠加的激活模式。本文介绍了一种方法,其允许自动检测在同时执行有节奏和离散的电动机任务期间出现的相位事件。基于非线性信号模型,通过使用双窗琴方案和广义似然比测试,将事件识别为原始EMG信号方差的特征变化。两者都可以检测到与节奏运动有关的突出的EMG活性的初始动态活性以及指示分离收缩的起始活动的突出的EMG活性。讨论了由由确定性控制模式调制的独立高斯随机变量序列建模EMG的问题。

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