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A masked least-squares smoothing procedure for artifact reduction in scanning-EMG recordings

机译:用于减少扫描心电图记录中伪影的蒙版最小二乘平滑程序

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摘要

Scanning-EMG is an electrophysiological technique in which the electrical activity of the motor unit is recorded at multiple points along a corridor crossing the motor unit territory. Correct analysis of the scanning-EMG signal requires prior elimination of interference from nearby motor units. Although the traditional processing based on the median filtering is effective in removing such interference, it distorts the physiological waveform of the scanning-EMG signal. In this study, we describe a new scanning-EMG signal processing algorithm that preserves the physiological signal waveform while effectively removing interference from other motor units. To obtain a cleaned-up version of the scanning signal, the masked least-squares smoothing (MLSS) algorithm recalculates and replaces each sample value of the signal using a least-squares smoothing in the spatial dimension, taking into account the information of only those samples that are not contaminated with activity of other motor units. The performance of the new algorithm with simulated scanning-EMG signals is studied and compared with the performance of the median algorithm and tested with real scanning signals. Results show that the MLSS algorithm distorts the waveform of the scanning-EMG signal much less than the median algorithm (approximately 3.5 dB gain), being at the same time very effective at removing interference components. >Graphical AbstractThe raw scanning-EMG signal (left figure) is processed by the MLSS algorithm in order to remove the artifact interference. Firstly, artifacts are detected from the raw signal, obtaining a validity mask (central figure) that determines the samples that have been contaminated by artifacts. Secondly, a least-squares smoothing procedure in the spatial dimension is applied to the raw signal using the not contaminated samples according to the validity mask. The resulting MLSS-processed scanning-EMG signal (right figure) is clean of artifact interference.
机译:扫描电动势是一种电生理技术,其中沿跨过电机单元区域的走廊的多个点记录了电机单元的电活动。正确分析EMG扫描信号需要事先消除附近电机单元的干扰。尽管基于中值滤波的传统处理在消除此类干扰方面很有效,但它会使扫描EMG信号的生理波形失真。在这项研究中,我们描述了一种新的扫描-EMG信号处理算法,该算法可在保持生理信号波形的同时有效消除来自其他电机单元的干扰。为了获得扫描信号的清理版本,蒙版最小二乘平滑(MLSS)算法使用空间维度上的最小二乘平滑来重新计算并替换信号的每个样本值,仅考虑那些信息未被其他电机活动污染的样品。研究了带有模拟扫描EMG信号的新算法的性能,并与中值算法的性能进行了比较,并用实际扫描信号进行了测试。结果表明,MLSS算法使扫描-EMG信号的波形失真小于中值算法(增益约为3.5 dB),同时在消除干扰分量方面非常有效。 <!-fig ft0-> <!-fig @ position =“ anchor” mode =文章f4-> <!-fig mode =“ anchred” f5-> >图形摘要<!- fig / graphic | fig / alternatives / graphic mode =“ anchored” m1-> <!-标题a7->原始扫描EMG信号(左图)由MLSS算法处理,以消除伪影干扰。首先,从原始信号中检测出伪影,从而获得确定已被伪影污染的样本的有效性掩码(中心图)。其次,根据有效性掩码,使用未污染的样本将空间维度的最小二乘平滑过程应用于原始信号。最终的MLSS处理的扫描EMG信号(右图)没有伪影干扰。

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