Estimating and minimizing movement artifacts in surface electromyogram

IF 2 4区 医学 Q3 NEUROSCIENCES Journal of Electromyography and Kinesiology Pub Date : 2023-06-01 DOI:10.1016/j.jelekin.2023.102778
Ilhan Karacan , Betilay Topkara Arslan , Ayşe Karaoglu , Tugba Aydin , Simon Gray , Pekcan Ungan , Kemal S. Türker
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Abstract

While recording surface electromyography [sEMG], it is possible to record the electrical activities coming from the muscles and transients in the half-cell potential at the electrode–electrolyte interface due to micromovements of the electrode–skin interface. Separating the two sources of electrical activity usually fails due to the overlapping frequency characteristics of the signals. This paper aims to develop a method that detects movement artifacts and suggests a minimization technique. Towards that aim, we first estimated the frequency characteristics of movement artifacts under various static and dynamic experimental conditions. We found that the extent of the movement artifact depended on the nature of the movement and varied from person to person. Our study's highest movement artifact frequency for the stand position was 10 Hz, tiptoe 22, walk 32, run 23, jump from box 41, and jump up and down 40 Hz. Secondly, using a 40 Hz highpass filter, we cut out most of the frequencies belonging to the movement artifacts. Finally, we checked whether the latencies and amplitudes of reflex and direct muscle responses were still observed in the highpass-filtered sEMG. We showed that the 40 Hz highpass filter did not significantly alter reflex and direct muscle variables. Therefore, we recommend that researchers who use sEMG under similar conditions employ the recommended level of highpass filtering to reduce movement artifacts from their records. However, suppose different movement conditions are used. In that case, it is best to estimate the frequency characteristics of the movement artifact before applying any highpass filtering to minimize movement artifacts and their harmonics from sEMG.

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表面肌电图中运动伪影的估计和最小化
在记录表面肌电图[sEMG]时,可以记录肌肉的电活动以及由于电极-皮肤界面的微运动而导致的电极-电解质界面半细胞电位的瞬态。由于信号的频率特性重叠,分离两个电活动源通常失败。本文旨在开发一种检测运动伪影的方法,并提出一种最小化技术。为此,我们首先估计了在各种静态和动态实验条件下运动伪影的频率特性。我们发现,运动伪影的程度取决于运动的性质,并且因人而异。我们的研究中,站立姿势的最高运动伪像频率为10 Hz,踮起脚尖22次,走路32次,跑步23次,跳箱41次,上下跳跃40 Hz。其次,使用40Hz高通滤波器,我们去除了属于运动伪影的大部分频率。最后,我们检查了在高通滤波sEMG中是否仍然观察到反射和直接肌肉反应的潜伏期和振幅。我们发现40Hz高通滤波器不会显著改变反射和直接肌肉变量。因此,我们建议在类似条件下使用sEMG的研究人员采用推荐级别的高通滤波,以减少记录中的运动伪影。但是,假设使用不同的运动条件。在这种情况下,最好在应用任何高通滤波之前估计运动伪影的频率特性,以最小化sEMG的运动伪影及其谐波。
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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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