Literature DB >> 9291030

Fractal analysis of surface EMG signals from the biceps.

V Gupta1, S Suryanarayanan, N P Reddy.   

Abstract

Nonlinear analysis techniques are necessary to understand the complexity of the EMG. The purpose of the present study was to determine the fractal dimension of surface EMG obtained from the biceps brachii of normal subjects during isokinetic flexion-extension of the arm. The measurements were obtained with different loading conditions on the arm and for various rates of flexion-extension. Fractal dimensions of the surface EMG signals were calculated for each of these conditions. ANOVA results showed statistically significant differences between the fractal dimensions calculated for different loading conditions and rates of flexion-extensin (P < or = 0.005). Linear regression analysis showed a correlation coefficient of 0.99 between the fractal dimension and the load, and a correlation coefficient of 0.98 between the fractal dimension and the rate of flexion-extension. The results of the study show that the fractal dimension can be used along with other parameters to characterize the EMG signal.

Mesh:

Year:  1997        PMID: 9291030     DOI: 10.1016/s1386-5056(97)00029-4

Source DB:  PubMed          Journal:  Int J Med Inform        ISSN: 1386-5056            Impact factor:   4.046


  14 in total

1.  Classification of surface EMG signal with fractal dimension.

Authors:  Xiao Hu; Zhi-zhong Wang; Xiao-mei Ren
Journal:  J Zhejiang Univ Sci B       Date:  2005-08       Impact factor: 3.066

2.  Characterization of surface EMG signals using improved approximate entropy.

Authors:  Wei-ting Chen; Zhi-zhong Wang; Xiao-mei Ren
Journal:  J Zhejiang Univ Sci B       Date:  2006-10       Impact factor: 3.066

3.  The effect of single-pulse transcranial magnetic stimulation and peripheral nerve stimulation on complexity of EMG signal: fractal analysis.

Authors:  M Cukic; J Oommen; D Mutavdzic; N Jorgovanovic; M Ljubisavljevic
Journal:  Exp Brain Res       Date:  2013-05-08       Impact factor: 1.972

4.  Decoding subtle forearm flexions using fractal features of surface electromyogram from single and multiple sensors.

Authors:  Sridhar Poosapadi Arjunan; Dinesh Kant Kumar
Journal:  J Neuroeng Rehabil       Date:  2010-10-21       Impact factor: 4.262

Review 5.  The role of the circadian system in fractal neurophysiological control.

Authors:  Benjamin R Pittman-Polletta; Frank A J L Scheer; Matthew P Butler; Steven A Shea; Kun Hu
Journal:  Biol Rev Camb Philos Soc       Date:  2013-04-10

6.  Evaluation of central and peripheral fatigue in the quadriceps using fractal dimension and conduction velocity in young females.

Authors:  Matteo Beretta-Piccoli; Giuseppe D'Antona; Marco Barbero; Beth Fisher; Christina M Dieli-Conwright; Ron Clijsen; Corrado Cescon
Journal:  PLoS One       Date:  2015-04-16       Impact factor: 3.240

7.  The Analysis of the Influence of Odorant's Complexity on Fractal Dynamics of Human Respiration.

Authors:  Hamidreza Namazi; Amin Akrami; Vladimir V Kulish
Journal:  Sci Rep       Date:  2016-05-31       Impact factor: 4.379

8.  Analyzing surface EMG signals to determine relationship between jaw imbalance and arm strength loss.

Authors:  Khoa Truong Quang Dang; Hoa Le Minh; Hai Nguyen Thanh; Toi Vo Van
Journal:  Biomed Eng Online       Date:  2012-08-22       Impact factor: 2.819

9.  A computational model to investigate the effect of pennation angle on surface electromyogram of Tibialis Anterior.

Authors:  Diptasree Maitra Ghosh; Dinesh Kumar; Sridhar Poosapadi Arjunan; Ariba Siddiqi; Ramakrishnan Swaminathan
Journal:  PLoS One       Date:  2017-12-07       Impact factor: 3.240

10.  Navigating features: a topologically informed chart of electromyographic features space.

Authors:  Angkoon Phinyomark; Rami N Khushaba; Esther Ibáñez-Marcelo; Alice Patania; Erik Scheme; Giovanni Petri
Journal:  J R Soc Interface       Date:  2017-12       Impact factor: 4.118

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