Literature DB >> 15664148

Comparison of Fourier and wavelet transform procedures for examining the mechanomyographic and electromyographic frequency domain responses during fatiguing isokinetic muscle actions of the biceps brachii.

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

Abstract

The primary purpose of the present study was to compare the fast Fourier transform (FFT) with the discrete wavelet transform (DWT) for determining the mechanomyographic (MMG) and electromyographic (EMG) center frequency [mean power frequency (mpf), median frequency (mdf), or wavelet center frequency (cf)] patterns during fatiguing isokinetic muscle actions of the biceps brachii. Seven men (mean+/-SD age=23+/-3 years) volunteered to perform 50 consecutive maximal, concentric isokinetic muscle actions of the dominant forearm flexors at a velocity of 180 degrees s(-1). Non-parametric "run" tests indicated significant (p<0.05) trends in the MMG and EMG signals for the 5th, 25th, and 45th muscle actions for all subjects, thereby confirming non-stationarity of the MMG and EMG signals. There were significant (p<0.05) correlations among the average normalized mpf, mdf, and cf values for contractions 1-50 for both MMG (r=0.671-0.935) and EMG (r=0.956-0.987). Polynomial regression analyses demonstrated quadratic decreases in normalized MMG mpf (R2=0.439), MMG mdf (R2=0.258), MMG cf (R2=0.359), EMG mpf (R2=0.952), EMG mdf (R2=0.914) and EMG cf (R2=0.888) across repetitions. The primary finding of this study was the similarity in the mpf, mdf, and cf patterns for both MMG and EMG, which suggested that, despite the concerns over non-stationarity, Fourier based methods are acceptable for determining the patterns for normalized MMG and EMG center frequency during fatiguing dynamic muscle actions at moderate velocities.

Entities:  

Mesh:

Year:  2005        PMID: 15664148     DOI: 10.1016/j.jelekin.2004.08.007

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


  20 in total

1.  Determination of Fatigue Following Maximal Loaded Treadmill Exercise by Using Wavelet Packet Transform Analysis and MLPNN from MMG-EMG Data Combinations.

Authors:  Gürkan Bilgin; I Ethem Hindistan; Y Gül Özkaya; Etem Köklükaya; Övünç Polat; Ömer H Çolak
Journal:  J Med Syst       Date:  2015-08-15       Impact factor: 4.460

2.  Changes in surface EMG assessed by discrete wavelet transform during maximal isometric voluntary contractions following supramaximal cycling.

Authors:  Luis Peñailillo; Rony Silvestre; Kazunori Nosaka
Journal:  Eur J Appl Physiol       Date:  2012-09-23       Impact factor: 3.078

3.  Squat, stoop, or semi-squat: a comparative experiment on lifting technique.

Authors:  Zhenglun Wang; Lei Wu; Jingzhi Sun; Lihua He; Sheng Wang; Lei Yang
Journal:  J Huazhong Univ Sci Technolog Med Sci       Date:  2012-08-11

4.  Muscle fatigue detection in EMG using time-frequency methods, ICA and neural networks.

Authors:  Abdulhamit Subasi; M Kemal Kiymik
Journal:  J Med Syst       Date:  2009-04-28       Impact factor: 4.460

5.  The application of Hilbert-Huang transform in the analysis of muscle fatigue during cyclic dynamic contractions.

Authors:  Vedran Srhoj-Egekher; Mario Cifrek; Vladimir Medved
Journal:  Med Biol Eng Comput       Date:  2010-12-09       Impact factor: 2.602

6.  Changes in muscle activity and kinematics of highly trained cyclists during fatigue.

Authors:  Jonathan B Dingwell; Jason E Joubert; Fernando Diefenthaeler; Joel D Trinity
Journal:  IEEE Trans Biomed Eng       Date:  2008-11       Impact factor: 4.538

7.  Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury.

Authors:  Jannatul Naeem; Nur Azah Hamzaid; Md Anamul Islam; Amelia Wong Azman; Manfred Bijak
Journal:  Med Biol Eng Comput       Date:  2019-01-28       Impact factor: 2.602

8.  Nonlinear smooth orthogonal decomposition of kinematic features of sawing reconstructs muscle fatigue evolution as indicated by electromyography.

Authors:  David B Segala; Deanna H Gates; Jonathan B Dingwell; David Chelidze
Journal:  J Biomech Eng       Date:  2011-03       Impact factor: 1.899

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.  Slow-time changes in human EMG muscle fatigue states are fully represented in movement kinematics.

Authors:  Miao Song; David B Segala; Jonathan B Dingwell; David Chelidze
Journal:  J Biomech Eng       Date:  2009-02       Impact factor: 1.899

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.