MUedit 教程:从肌电信号中识别和分析运动单元放电时间的开源软件。

IF 2 4区 医学 Q3 NEUROSCIENCES
Simon Avrillon , François Hug , Stuart N Baker , Ciara Gibbs , Dario Farina
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引用次数: 0

摘要

我们介绍了开源软件 MUedit,并介绍了该软件用于从多通道系统记录的各类肌电图(EMG)信号中识别运动单元的放电时间。MUedit 采用盲源分离方法进行 EMG 分解。随后,用户可以显示估计的运动单元脉冲串,并检查自动检测放电时间的准确性。必要时,用户可以纠正放电时间的自动检测,并使用更新的分离向量重新计算运动单元脉冲串。在此,我们提供了一个开源软件和教程,指导用户 (i) 分解算法的参数和步骤,以及 (ii) 手动编辑运动单元脉冲串。此外,我们还提供了通过表面电极网格和肌肉内电极阵列记录的模拟和实验肌电信号,以对 MUedit 的性能进行基准测试。最后,我们讨论了盲源分离法在研究肌肉强直收缩时运动单元行为方面的优势和局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tutorial on MUedit: An open-source software for identifying and analysing the discharge timing of motor units from electromyographic signals

We introduce the open-source software MUedit and we describe its use for identifying the discharge timing of motor units from all types of electromyographic (EMG) signals recorded with multi-channel systems. MUedit performs EMG decomposition using a blind-source separation approach. Following this, users can display the estimated motor unit pulse trains and inspect the accuracy of the automatic detection of discharge times. When necessary, users can correct the automatic detection of discharge times and recalculate the motor unit pulse train with an updated separation vector. Here, we provide an open-source software and a tutorial that guides the user through (i) the parameters and steps of the decomposition algorithm, and (ii) the manual editing of motor unit pulse trains. Further, we provide simulated and experimental EMG signals recorded with grids of surface electrodes and intramuscular electrode arrays to benchmark the performance of MUedit. Finally, we discuss advantages and limitations of the blind-source separation approach for the study of motor unit behaviour during tonic muscle contractions.

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来源期刊
CiteScore
4.70
自引率
8.00%
发文量
70
审稿时长
74 days
期刊介绍: Journal of Electromyography & Kinesiology is the primary source for outstanding original articles on the study of human movement from muscle contraction via its motor units and sensory system to integrated motion through mechanical and electrical detection techniques. As the official publication of the International Society of Electrophysiology and Kinesiology, the journal is dedicated to publishing the best work in all areas of electromyography and kinesiology, including: control of movement, muscle fatigue, muscle and nerve properties, joint biomechanics and electrical stimulation. Applications in rehabilitation, sports & exercise, motion analysis, ergonomics, alternative & complimentary medicine, measures of human performance and technical articles on electromyographic signal processing are welcome.
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