Literature DB >> 15605861

Volume conduction in an anatomically based surface EMG model.

Madeleine M Lowery1, Nikolay S Stoykov, Julius P A Dewald, Todd A Kuiken.   

Abstract

A finite-element model to simulate surface electromyography (EMG) in a realistic human upper arm is presented. The model is used to explore the effect of limb geometry on surface-detected muscle fiber action potentials. The model was based on magnetic resonance images of the subject's upper arm and includes both resistive and capacitive material properties. To validate the model geometry, experimental and simulated potentials were compared at different electrode sites during the application of a subthreshold sinusoidal current source to the skin surface. Of the material properties examined, the closest approximation to the experimental data yielded a mean root-mean-square (rms) error of the normalized surface potential of 18% or 27%, depending on the site of the applied source. Surface-detected action potentials simulated using the realistic volume conductor model and an idealized cylindrical model based on the same limb geometry were then compared. Variation in the simulated limb geometry had a considerable effect on action potential shape. However, the rate of decay of the action potential amplitude with increasing distance from the fiber was similar in both models. Inclusion of capacitive material properties resulted in temporal low-pass filtering of the surface action potentials. This effect was most pronounced in the end-effect components of action potentials detected at locations far from the active fiber. It is concluded that accurate modeling of the limb geometry, asymmetry, tissue capacitance and fiber curvature is important when the specific action potential shapes are of interest. However, if the objective is to examine more qualitative features of the surface EMG signal, then an idealized volume conductor model with appropriate tissue thicknesses provides a close approximation.

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Year:  2004        PMID: 15605861     DOI: 10.1109/TBME.2004.836494

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  10 in total

1.  Predicting electromyographic signals under realistic conditions using a multiscale chemo-electro-mechanical finite element model.

Authors:  Mylena Mordhorst; Thomas Heidlauf; Oliver Röhrle
Journal:  Interface Focus       Date:  2015-04-06       Impact factor: 3.906

2.  Electromyographic amplitude versus torque relationships are different in young versus postmenopausal females and are related to muscle mass after controlling for bodyweight.

Authors:  Nile F Banks; Emily M Rogers; Nathaniel D M Jenkins
Journal:  Eur J Appl Physiol       Date:  2020-10-29       Impact factor: 3.078

Review 3.  Endurance-exercise training adaptations in spinal motoneurones: potential functional relevance to locomotor output and assessment in humans.

Authors:  Kevin E Power; Evan J Lockyer; Alberto Botter; Taian Vieira; Duane C Button
Journal:  Eur J Appl Physiol       Date:  2022-02-28       Impact factor: 3.078

4.  Nonhomogeneous volume conduction effects affecting needle electromyography: an analytical and simulation study.

Authors:  Xuesong Luo; Shaoping Wang; Seward B Rutkove; Benjamin Sanchez
Journal:  Physiol Meas       Date:  2021-12-28       Impact factor: 2.833

5.  A practice of caution: spontaneous action potentials or artifactual spikes?

Authors:  Faezeh Jahanmiri-Nezhad; Xiaoyan Li; William Zev Rymer; Ping Zhou
Journal:  J Neuroeng Rehabil       Date:  2015-01-13       Impact factor: 4.262

6.  Influence of different geometric representations of the volume conductor on nerve activation during electrical stimulation.

Authors:  José Gómez-Tames; José González; Wenwei Yu
Journal:  Comput Math Methods Med       Date:  2014-09-09       Impact factor: 2.238

7.  Quantum theory of mass potentials.

Authors:  Dmitriy Melkonian; Terry Blumenthal; Edward Barin
Journal:  PLoS One       Date:  2018-07-05       Impact factor: 3.240

8.  Anatomically accurate model of EMG during index finger flexion and abduction derived from diffusion tensor imaging.

Authors:  Diego Pereira Botelho; Kathleen Curran; Madeleine M Lowery
Journal:  PLoS Comput Biol       Date:  2019-08-29       Impact factor: 4.475

9.  Towards a Simplified Estimation of Muscle Activation Pattern from MRI and EMG Using Electrical Network and Graph Theory.

Authors:  Enrico Piovanelli; Davide Piovesan; Shouhei Shirafuji; Becky Su; Natsue Yoshimura; Yousuke Ogata; Jun Ota
Journal:  Sensors (Basel)       Date:  2020-01-28       Impact factor: 3.576

10.  A Finite Element Model Approach to Determine the Influence of Electrode Design and Muscle Architecture on Myoelectric Signal Properties.

Authors:  A Teklemariam; E F Hodson-Tole; N D Reeves; N P Costen; G Cooper
Journal:  PLoS One       Date:  2016-02-17       Impact factor: 3.240

  10 in total

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