Literature DB >> 25844148

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

Mylena Mordhorst1, Thomas Heidlauf1, Oliver Röhrle1.   

Abstract

This paper presents a novel multiscale finite element-based framework for modelling electromyographic (EMG) signals. The framework combines (i) a biophysical description of the excitation-contraction coupling at the half-sarcomere level, (ii) a model of the action potential (AP) propagation along muscle fibres, (iii) a continuum-mechanical formulation of force generation and deformation of the muscle, and (iv) a model for predicting the intramuscular and surface EMG. Owing to the biophysical description of the half-sarcomere, the model inherently accounts for physiological properties of skeletal muscle. To demonstrate this, the influence of membrane fatigue on the EMG signal during sustained contractions is investigated. During a stimulation period of 500 ms at 100 Hz, the predicted EMG amplitude decreases by 40% and the AP propagation velocity decreases by 15%. Further, the model can take into account contraction-induced deformations of the muscle. This is demonstrated by simulating fixed-length contractions of an idealized geometry and a model of the human tibialis anterior muscle (TA). The model of the TA furthermore demonstrates that the proposed finite element model is capable of simulating realistic geometries, complex fibre architectures, and can include different types of heterogeneities. In addition, the TA model accounts for a distributed innervation zone, different fibre types and appeals to motor unit discharge times that are based on a biophysical description of the α motor neurons.

Entities:  

Keywords:  bidomain; biophysical modelling; electromyography; fatigue; finite element method; skeletal muscle

Year:  2015        PMID: 25844148      PMCID: PMC4342944          DOI: 10.1098/rsfs.2014.0076

Source DB:  PubMed          Journal:  Interface Focus        ISSN: 2042-8898            Impact factor:   3.906


  48 in total

1.  A novel approach for precise simulation of the EMG signal detected by surface electrodes.

Authors:  D Farina; R Merletti
Journal:  IEEE Trans Biomed Eng       Date:  2001-06       Impact factor: 4.538

2.  The presence of unknown layer of skin and fat is an obstacle to a correct estimation of the motor unit size from surface detected potentials.

Authors:  G V Dimitrov; C Disselhorst-Klug; N A Dimitrova; A Trachterna; G Rau
Journal:  Electromyogr Clin Neurophysiol       Date:  2002-06

3.  Measurement of muscle contraction with ultrasound imaging.

Authors:  P W Hodges; L H M Pengel; R D Herbert; S C Gandevia
Journal:  Muscle Nerve       Date:  2003-06       Impact factor: 3.217

4.  Neither high-pass filtering nor mathematical differentiation of the EMG signals can considerably reduce cross-talk.

Authors:  N A Dimitrova; G V Dimitrov; O A Nikitin
Journal:  J Electromyogr Kinesiol       Date:  2002-08       Impact factor: 2.368

Review 5.  Decoding the neural drive to muscles from the surface electromyogram.

Authors:  Dario Farina; Ales Holobar; Roberto Merletti; Roger M Enoka
Journal:  Clin Neurophysiol       Date:  2010-05-04       Impact factor: 3.708

6.  Volume conduction in an anatomically based surface EMG model.

Authors:  Madeleine M Lowery; Nikolay S Stoykov; Julius P A Dewald; Todd A Kuiken
Journal:  IEEE Trans Biomed Eng       Date:  2004-12       Impact factor: 4.538

7.  Reconstruction of the action potential of frog sartorius muscle.

Authors:  R H Adrian; L D Peachey
Journal:  J Physiol       Date:  1973-11       Impact factor: 5.182

8.  An allosteric model of the molecular interactions of excitation-contraction coupling in skeletal muscle.

Authors:  E Ríos; M Karhanek; J Ma; A González
Journal:  J Gen Physiol       Date:  1993-09       Impact factor: 4.086

9.  Electromyography and sonomyography analysis of the tibialis anterior: a cross sectional study.

Authors:  Antonio I Cuesta-Vargas; Maria Ruiz-Muñoz
Journal:  J Foot Ankle Res       Date:  2014-02-08       Impact factor: 2.303

10.  A multiscale chemo-electro-mechanical skeletal muscle model to analyze muscle contraction and force generation for different muscle fiber arrangements.

Authors:  Thomas Heidlauf; Oliver Röhrle
Journal:  Front Physiol       Date:  2014-12-23       Impact factor: 4.566

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  7 in total

1.  The formation of extracellular potentials over the innervation zone: Are these potentials affected by changes in fibre membrane properties?

Authors:  Javier Rodriguez-Falces
Journal:  Med Biol Eng Comput       Date:  2016-04-05       Impact factor: 2.602

2.  Multiscale musculoskeletal modelling, data-model fusion and electromyography-informed modelling.

Authors:  J Fernandez; J Zhang; T Heidlauf; M Sartori; T Besier; O Röhrle; D Lloyd
Journal:  Interface Focus       Date:  2016-04-06       Impact factor: 3.906

3.  Mechanisms of in vivo muscle fatigue in humans: investigating age-related fatigue resistance with a computational model.

Authors:  Damien M Callahan; Brian R Umberger; Jane A Kent
Journal:  J Physiol       Date:  2016-03-02       Impact factor: 5.182

4.  The Role of Parvalbumin, Sarcoplasmatic Reticulum Calcium Pump Rate, Rates of Cross-Bridge Dynamics, and Ryanodine Receptor Calcium Current on Peripheral Muscle Fatigue: A Simulation Study.

Authors:  Oliver Röhrle; Verena Neumann; Thomas Heidlauf
Journal:  Comput Math Methods Med       Date:  2016-10-20       Impact factor: 2.238

5.  Enabling Detailed, Biophysics-Based Skeletal Muscle Models on HPC Systems.

Authors:  Chris P Bradley; Nehzat Emamy; Thomas Ertl; Dominik Göddeke; Andreas Hessenthaler; Thomas Klotz; Aaron Krämer; Michael Krone; Benjamin Maier; Miriam Mehl; Tobias Rau; Oliver Röhrle
Journal:  Front Physiol       Date:  2018-07-12       Impact factor: 4.566

6.  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

7.  Morphological Computation Increases From Lower- to Higher-Level of Biological Motor Control Hierarchy.

Authors:  Daniel F B Haeufle; Katrin Stollenmaier; Isabelle Heinrich; Syn Schmitt; Keyan Ghazi-Zahedi
Journal:  Front Robot AI       Date:  2020-10-21
  7 in total

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