Literature DB >> 16497517

Does the frequency content of the surface mechanomyographic signal reflect motor unit firing rates? A brief review.

Travis W Beck1, Terry J Housh, Glen O Johnson, Joel T Cramer, Joseph P Weir, Jared W Coburn, Moh H Malek.   

Abstract

The purpose of this review is to examine the literature that has investigated the potential relationship between mechanomyographic (MMG) frequency and motor unit firing rates. Several different experimental designs/methodologies have been used to address this issue, including: repetitive electrical stimulation, voluntary muscle actions in muscles with different fiber type compositions, fatiguing and non-fatiguing isometric or dynamic muscle actions, and voluntary muscle actions in young versus elderly subjects and healthy individuals versus subjects with a neuromuscular disease(s). Generally speaking, the results from these investigations have suggested that MMG frequency is related to the rate of motor unit activation and the contractile properties (contraction and relaxation times) of the muscle fibers. Other studies, however, have reported that MMG mean power frequency (MPF) does not always follow the expected pattern of firing rate modulation (e.g. motor unit firing rates generally increase with torque during isometric muscle actions, but MMG MPF may remain stable or even decrease). In addition, there are several factors that may affect the frequency content of the MMG signal during a voluntary muscle action (i.e. muscle stiffness, intramuscular fluid pressure, etc.), independent of changes in motor unit firing rates. Despite the potential influences of these factors, most of the evidence has suggested that the frequency domain of the MMG signal contains some information regarding motor unit firing rates. It is likely, however, that this information is qualitative, rather than quantitative in nature, and reflects the global motor unit firing rate, rather than the firing rates of a particular group of motor units.

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Year:  2006        PMID: 16497517     DOI: 10.1016/j.jelekin.2005.12.002

Source DB:  PubMed          Journal:  J Electromyogr Kinesiol        ISSN: 1050-6411            Impact factor:   2.368


  23 in total

1.  Effects of spinal anesthesia on resting metabolic rate and quadriceps mechanomyography.

Authors:  William Paul McKay; Brendan Lett; Philip D Chilibeck; Brian L Daku
Journal:  Eur J Appl Physiol       Date:  2009-04-09       Impact factor: 3.078

2.  Electrical and mechanical response of finger flexor muscles during voluntary isometric contractions in elite rock-climbers.

Authors:  Fabio Esposito; Eloisa Limonta; Emiliano Cè; Massimiliano Gobbo; Arsenio Veicsteinas; Claudio Orizio
Journal:  Eur J Appl Physiol       Date:  2008-10-01       Impact factor: 3.078

3.  Muscle-related differences in mechanomyography frequency-force relationships are model dependent.

Authors:  Trent J Herda; Michael A Cooper
Journal:  Med Biol Eng Comput       Date:  2015-03-25       Impact factor: 2.602

4.  Examination of a neural cross-over effect using resting mechanomyographic mean frequency from the vastus lateralis muscle in different resting positions following aerobic exercise.

Authors:  Nathan P Wages; Travis W Beck; Xin Ye; Joshua C Carr
Journal:  Eur J Appl Physiol       Date:  2016-03-12       Impact factor: 3.078

5.  Inter-individual variability in the patterns of responses for electromyography and mechanomyography during cycle ergometry using an RPE-clamp model.

Authors:  Kristen C Cochrane-Snyman; Terry J Housh; Cory M Smith; Ethan C Hill; Nathaniel D M Jenkins; Richard J Schmidt; Glen O Johnson
Journal:  Eur J Appl Physiol       Date:  2016-06-20       Impact factor: 3.078

6.  Passive stretching effects on electromechanical delay and time course of recovery in human skeletal muscle: new insights from an electromyographic and mechanomyographic combined approach.

Authors:  Fabio Esposito; Eloisa Limonta; Emiliano Cè
Journal:  Eur J Appl Physiol       Date:  2010-10-01       Impact factor: 3.078

7.  The effect of accelerometer location on the classification of single-site forearm mechanomyograms.

Authors:  Natasha Alves; Ervin Sejdić; Bhupinder Sahota; Tom Chau
Journal:  Biomed Eng Online       Date:  2010-06-10       Impact factor: 2.819

8.  Mechanomyographic parameter extraction methods: an appraisal for clinical applications.

Authors:  Morufu Olusola Ibitoye; Nur Azah Hamzaid; Jorge M Zuniga; Nazirah Hasnan; Ahmad Khairi Abdul Wahab
Journal:  Sensors (Basel)       Date:  2014-12-03       Impact factor: 3.576

Review 9.  Mechanomyogram for muscle function assessment: a review.

Authors:  Md Anamul Islam; Kenneth Sundaraj; R Badlishah Ahmad; Nizam Uddin Ahamed
Journal:  PLoS One       Date:  2013-03-11       Impact factor: 3.240

10.  Cross-talk in mechanomyographic signals from the forearm muscles during sub-maximal to maximal isometric grip force.

Authors:  Md Anamul Islam; Kenneth Sundaraj; R Badlishah Ahmad; Sebastian Sundaraj; Nizam Uddin Ahamed; Md Asraf Ali
Journal:  PLoS One       Date:  2014-05-06       Impact factor: 3.240

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