Analysis of motor unit spike trains estimated from high-density surface electromyography is highly reliable across operators
File(s)MUxtraction_R2_JEK_submitted_clean.docx (2.97 MB)
Accepted version
Author(s)
Type
Journal Article
Abstract
There is a growing interest in decomposing high-density surface electromyography (HDsEMG) into motor unit spike trains to improve knowledge on the neural control of muscle contraction. However, the reliability of decomposition approaches is sometimes questioned, especially because they require manual editing of the outputs. We aimed to assess the inter-operator reliability of the identification of motor unit spike trains. Eight operators with varying experience in HDsEMG decomposition were provided with the same data extracted using the convolutive kernel compensation method. They were asked to manually edit them following established procedures. Data included signals from three lower leg muscles and different submaximal intensities. After manual analysis, 126 ± 5 motor units were retained (range across operators: 119-134). A total of 3380 rate of agreement values were calculated (28 pairwise comparisons × 11 contractions/muscles × 4-28 motor units). The median rate of agreement value was 99.6%. Inter-operator reliability was excellent for both mean discharge rate and time at recruitment (intraclass correlation coefficient > 0.99). These results show that when provided with the same decomposed data and the same basic instructions, operators converge toward almost identical results. Our data have been made available so that they can be used for training new operators.
Date Issued
2021-06-01
Date Acceptance
2021-03-23
Citation
Journal of Electromyography and Kinesiology, 2021, 58, pp.102548-102548
ISSN
1050-6411
Publisher
Elsevier
Start Page
102548
End Page
102548
Journal / Book Title
Journal of Electromyography and Kinesiology
Volume
58
Copyright Statement
© 2021 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/33838590
PII: S1050-6411(21)00035-3
Subjects
Dataset
Decomposition
Editing
Electromyography
Neural drive
Publication Status
Published
Coverage Spatial
England
Article Number
ARTN 102548
Date Publish Online
2021-03-30