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Identification_of_Motor_Unit_Firings_in_H-Reflex_of_Soleus_Muscle_Recorded_by_High-Density_Surface_Electromyography.pdf (1.43 MB)

Identification of motor unit firings in H-reflex of soleus muscle recorded by high-density surface electromyography

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posted on 2023-01-31, 10:03 authored by Milos Kalc, Jakob SkarabotJakob Skarabot, Matjaz Divjak, Filip Urh, Matej Kramberger, Matjaz Vogrin, Ales Holobar

We developed and tested the methodology that supports the identification of individual motor unit (MU) firings from the Hoffman (or H) reflex recorded by surface high-density EMG (HD-EMG). Synthetic HD-EMG signals were constructed from simulated 10% to 90% of maximum voluntary contraction - MVC, followed by 100 simulated H-reflexes. In each H-reflex the MU firings were normally distributed with mean latency of 20 ms and standard deviations (SDLAT) ranging from 0.1 to 1.3 ms. Experimental H-reflexes were recorded from the soleus muscle of 12 men (33.6 ± 5.8 years) using HD-EMG array of 5×13 surface electrodes. Participants performed 15 to 20 s long voluntary plantarflexions with contraction levels ranging from 10% to 70% MVC. Afterwards, at least 60 H-reflexes were electrically elicited at three levels of background muscle activity: rest, 10% and 20% MVC. HD-EMGs of voluntary contractions were decomposed using the Convolution Kernel Compensation method to estimate the MU filters. When applied to HD-EMG signals with synthetic H reflexes, MU filters demonstrated high MU identification accuracy, especially for SDLAT > 0.3 ms. When applied to experimental H-reflex recordings, the MU filters identified 14.1 ± 12.1, 18.2 ± 12.1 and 20.8 ± 8.7 firings per H-reflex, with individual MU firing latencies of 35.9 ± 3.3, 35.1 ± 3.0 and 34.6 ± 3.3 ms for rest, 10% and 20% MVC background muscle activity, respectively. Standard deviation of MU latencies across experimental H-reflexes were 1.0 ± 0.8, 1.3 ± 1.1 and 1.5 ± 1.2 ms, in agreement with intramuscular EMG studies.

Funding

Slovenian Research Agency (Project J2-1731 and Programme funding P2-0041)

History

School

  • Sport, Exercise and Health Sciences

Published in

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Volume

31

Pages

119 - 129

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

2022-09-22

Publication date

2022-10-31

Copyright date

2022

ISSN

1534-4320

eISSN

1558-0210

Language

  • en

Depositor

Dr Jakob Skarabot. Deposit date: 22 September 2022

Ethics review number

0120-84/2020/4

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