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Motor_Unit_Identification_in_the_M_Waves_Recorded_by_High-Density_Electromyogram (1).pdf (2.3 MB)

Motor unit identification in the M waves recorded by high-density electromyogram

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posted on 2023-04-21, 13:10 authored by Milos Kalc, Jakob SkarabotJakob Skarabot, Matjaz Divjak, Filip Urh, Matej Kramberger, Matjaz Vogrin, Ales Holobar

Objective: We describe and test the methodology supporting the identification of individual motor unit (MU) firings in the motor response (M wave) to percutaneous nerve stimulation recorded by surface high-density electromyography (HD-EMG) on synthetic and experimental data. Methods: A set of simulated voluntary contractions followed by 100 simulated M waves with a normal distribution (MU mean firing latency: 10 ms, Standard Deviation - SDLAT 0.1-1.3 ms) constituted the synthetic signals. In experimental condition, at least 52 progressively increasing M waves were elicited in the soleus muscle of 12 males, at rest (REST), and at 10% (C10) and 20% (C20) of maximal voluntary contraction (MVC). The MU decomposition filters were identified from 15-20 s long isometric plantar flexions performed at 10-70% of MVC and, afterwards, applied to M waves. Results: Synthetic signal analysis demonstrated high accuracy of MU identification in M waves (precision ≥ 85%). In experimental conditions 42.6 ± 11.2 MUs per participant were identified from voluntary contractions. When the MU filters were applied to the M wave recordings, 28.4 ± 14.3, 23.7 ± 14.9 and 20.2 ± 13.5 MU firings were identified in the maximal M waves, with individual MU firing latencies of 10.0 ± 2.8 ( SDLAT : 1.2 ± 1.2), 9.6 ± 3.0 ( SDLAT : 1.5 ± 1.3) and 10.1 ± 3.7 ( SDLAT : 1.7 ± 1.6) ms in REST, C10 and C20 conditions, respectively. Conclusion and significance: We present evidence that supports the feasibility of identifying MU firings in M waves recorded by HD-EMG.

Funding

Decomposition of compound muscle action potentials

Slovenian Research Agency

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Slovenian Research Agency (Grant Number: P2-0041)

History

School

  • Sport, Exercise and Health Sciences

Published in

IEEE Transactions on Biomedical Engineering

Volume

70

Issue

5

Pages

1662 - 1672

Publisher

Institute of Electrical and Electronics Engineers

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by IEEE under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

2022-11-22

Publication date

2022-11-28

Copyright date

2022

ISSN

0018-9294

eISSN

1558-2531

Language

  • en

Depositor

Dr Jakob Skarabot. Deposit date: 22 November 2022

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