Evidence map›Paper›PMID 34677312›Full record

ArticleBiosensors2021

Neurophysiological Factors Affecting Muscle Innervation Zone Estimation Using Surface EMG: A Simulation Study.

Chengjun Huang, Maoqi Chen, Xiaoyan Li, Yingchun Zhang, Sheng Li, Ping Zhou

Abstract read
In one paragraph

Article in Biosensors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Chengjun HuangGuangdong Work Injury Rehabilitation Center, Guangzhou 510970, China.
Maoqi ChenFaculty of Rehabilitation Engineering, University of Health and Rehabilitation Sciences, Qingdao 266024, China.ORCID 0000-0001-9681-1876
Xiaoyan LiDepartment of Bioengineering, University of Maryland, College Park, MD 20742, USA.
Yingchun ZhangDepartment of Biomedical Engineering, University of Houston, Houston, TX 77204, USA.
Sheng LiDepartment of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Ping ZhouFaculty of Rehabilitation Engineering, University of Health and Rehabilitation Sciences, Qingdao 266024, China.

Funding

High-Density surface EMG based CMAP scan for motor unit number estimationR21NS113716 · NINDS · MEDICAL COLLEGE OF WISCONSIN · PI BARKHAUS, PAUL E, LI, XIAOYAN · 2020 to 2020
$432k
Natural Science Foundation of Shandong Province ZR2020KF012NINDS NIH HHS R21 NS113716
6 · The paper itself

Abstract

Surface electromyography (EMG) recorded by a linear or 2-dimensional electrode array can be used to estimate the location of muscle innervation zones (IZ). There are various neurophysiological factors that may influence surface EMG and thus potentially compromise muscle IZ estimation. The objective of this study was to evaluate how surface-EMG-based IZ estimation might be affected by different factors, including varying degrees of motor unit (MU) synchronization in the case of single or double IZs. The study was performed by implementing a model simulating surface EMG activity. Three different MU synchronization conditions were simulated, namely no synchronization, medium level synchronization, and complete synchronization analog to M wave. Surface EMG signals recorded by a 2-dimensional electrode array were simulated from a muscle with single and double IZs, respectively. For each situation, the IZ was estimated from surface EMG and compared with the one used in the model for performance evaluation. For the muscle with only one IZ, the estimated IZ location from surface EMG was consistent with the one used in the model for all the three MU synchronization conditions. For the muscle with double IZs, at least one IZ was appropriately estimated from interference surface EMG when there was no MU synchronization. However, the estimated IZ was different from either of the two IZ locations used in the model for the other two MU synchronization conditions. For muscles with a single IZ, MU synchronization has little effect on IZ estimation from electrode array surface EMG. However, caution is required for multiple IZ muscles since MU synchronization might lead to false IZ estimation.

Indexed as

Computer SimulationElectrodesElectromyographyHumansMuscle, SkeletalSignal Processing, Computer-Assistedelectrode arrayinnervation zonemotor unit synchronizationM wavesimulationsurface electromyography

Identifiers

PMID34677312
PMCPMC8534086

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.