Evidence map›Paper›PMID 38360967›Full record

ArticleScientific reports2024

Inferring position of motor units from high-density surface EMG.

Jonathan Lundsberg, Anders Björkman, Nebojsa Malesevic, Christian Antfolk

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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.

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2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jonathan LundsbergDepartment of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden. jonathan.lundsberg@bme.lth.se.
Anders BjörkmanDepartment of Hand Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden.
Nebojsa MalesevicDepartment of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden.
Christian AntfolkDepartment of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden. christian.antfolk@bme.lth.se.

Funding

Vetenskapsrådet DNR 2019-05601
6 · The paper itself

Abstract

The spatial distribution of muscle fibre activity is of interest in guiding therapy and assessing recovery of motor function following injuries of the peripheral or central nervous system. This paper presents a new method for stable estimation of motor unit territory centres from high-density surface electromyography (HDsEMG). This completely automatic process applies principal component compression and a rotatable Gaussian surface fit to motor unit action potential (MUAP) distributions to map the spatial distribution of motor unit activity. Each estimated position corresponds to the signal centre of the motor unit territory. Two subjects were used to test the method on forearm muscles, using two different approaches. With the first dataset, motor units were identified by decomposition of intramuscular EMG and the centre position of each motor unit territory was estimated from synchronized HDsEMG data. These positions were compared to the positions of the intramuscular fine wire electrodes with depth measured using ultrasound. With the second dataset, decomposition and motor unit localization was done directly on HDsEMG data, during specific muscle contractions. From the first dataset, the mean estimated depth of the motor unit centres were 8.7, 11.6, and 9.1 mm, with standard deviations 0.5, 0.1, and 1.3 mm, and the respective depths of the fine wire electrodes were 8.4, 15.8, and 9.1 mm. The second dataset generated distinct spatial distributions of motor unit activity which were used to identify the regions of different muscles of the forearm, in a 3-dimensional and projected 2-dimensional view. In conclusion, a method is presented which estimates motor unit centre positions from HDsEMG. The study demonstrates the shifting spatial distribution of muscle fibre activity between different efforts, which could be used to assess individual muscles on a motor unit level.

Indexed as

Muscle ContractionMuscle, SkeletalAction PotentialsElectrodesElectromyographyHumansMuscle Fibers, SkeletalElectromyographyMotor unit depthMotor unit localizationMotor unit spatial distribution

Identifiers

PMID38360967
PMCPMC10869353

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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.