title: Predicting electromyographic signals under realistic conditions using a multiscale chemo-electro-mechanical finite element model.
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.
authors.affiliation: Institute of Applied Mechanics (CE) , University of Stuttgart , Pfaffenwaldring 7, 70569 Stuttgart , Germany ; Stuttgart Research Centre for Simulation Technology , Pfaffenwaldring 5a, 70569 Stuttgart , Germany.
authors.forename: Mylena
authors.initials: M
authors.lastname: Mordhorst
authors.affiliation: Institute of Applied Mechanics (CE) , University of Stuttgart , Pfaffenwaldring 7, 70569 Stuttgart , Germany ; Stuttgart Research Centre for Simulation Technology , Pfaffenwaldring 5a, 70569 Stuttgart , Germany.
authors.forename: Thomas
authors.identifier: 0000-0001-9381-1598
authors.initials: T
authors.lastname: Heidlauf
authors.affiliation: Institute of Applied Mechanics (CE) , University of Stuttgart , Pfaffenwaldring 7, 70569 Stuttgart , Germany ; Stuttgart Research Centre for Simulation Technology , Pfaffenwaldring 5a, 70569 Stuttgart , Germany.
authors.forename: Oliver
authors.initials: O
authors.lastname: Röhrle
keywords: bidomain; biophysical modelling; electromyography; fatigue; finite element method; skeletal muscle
references.citation: J Gen Physiol. 1993 Sep;102(3):449-81
references.pmid: 8245819
references.citation: Clin Neurophysiol. 2010 Oct;121(10):1616-23
references.pmid: 20444646
references.citation: J Electromyogr Kinesiol. 2002 Aug;12(4):235-46
references.pmid: 12121680
references.citation: IEEE Trans Biomed Eng. 2001 Jun;48(6):637-46
references.pmid: 11396594
references.citation: IEEE Trans Biomed Eng. 2004 Dec;51(12):2138-47
references.pmid: 15605861
references.citation: Front Physiol. 2014 Dec 23;5:498
references.pmid: 25566094
references.citation: J Foot Ankle Res. 2014 Feb 08;7(1):11
references.pmid: 24507748
references.citation: J Physiol. 1973 Nov;235(1):103-31
references.pmid: 4778131
references.citation: Electromyogr Clin Neurophysiol. 2002 Jun;42(4):231-41
references.pmid: 12056338
references.citation: Muscle Nerve. 2003 Jun;27(6):682-92
references.pmid: 12766979
references.citation: Ann Biomed Eng. 2006 Jul;34(7):1088-97
references.pmid: 16773461
references.citation: Clin Neurophysiol. 2009 Mar;120(3):551-62
references.pmid: 19208498
references.citation: IEEE Trans Biomed Eng. 2006 Apr;53(4):593-600
references.pmid: 16602565
references.citation: Comput Biol Med. 2013 Aug 1;43(7):942-52
references.pmid: 23489655
references.citation: J Neurophysiol. 1965 May;28:560-80
references.pmid: 14328454
references.citation: J Appl Physiol (1985). 2007 Aug;103(2):673-81
references.pmid: 17446411
references.citation: Muscle Nerve. 1988 Jul;11(7):714-9
references.pmid: 2457155
references.citation: J Neurophysiol. 1993 Dec;70(6):2470-88
references.pmid: 8120594
references.citation: IEEE Trans Biomed Eng. 2004 Mar;51(3):415-26
references.pmid: 15000373
references.citation: IEEE Trans Biomed Eng. 2006 Jul;53(7):1265-72
references.pmid: 16830931
references.citation: J Appl Physiol (1985). 2004 Apr;96(4):1486-95
references.pmid: 15016793
references.citation: J Muscle Res Cell Motil. 2007;28(6):293-313
references.pmid: 18080210
references.citation: IEEE Trans Biomed Eng. 2002 May;49(5):446-54
references.pmid: 12002176
references.citation: Comput Math Methods Med. 2013;2013:517287
references.pmid: 24348739
references.citation: J Neurosci Methods. 2009 Dec 15;185(1):143-50
references.pmid: 19747943
references.citation: J Gen Physiol. 1998 Sep;112(3):297-316
references.pmid: 9725890
references.citation: Clin Neurophysiol. 2008 Dec;119(12):2795-9
references.pmid: 18996046
references.citation: Eur Biophys J. 1999;28(4):317-29
references.pmid: 10394624
references.citation: Prog Biophys Mol Biol. 2011 Oct;107(1):32-47
references.pmid: 21762717
references.citation: J Neurophysiol. 2011 Nov;106(5):2688-97
references.pmid: 21795617
references.citation: IEEE Trans Biomed Eng. 2002 Nov;49(11):1260-9
references.pmid: 12450356
references.citation: IEEE Trans Biomed Eng. 2008 Jun;55(6):1647-57
references.pmid: 18714828
references.citation: J Electromyogr Kinesiol. 2009 Aug;19(4):667-75
references.pmid: 18387314
references.citation: Med Biol Eng Comput. 2004 Jul;42(4):467-76
references.pmid: 15320455
references.citation: J Physiol. 1952 Aug;117(4):500-44
references.pmid: 12991237
references.citation: Muscle Nerve. 1986 May;9(4):367-74
references.pmid: 3012329
references.citation: J Physiol. 1969 Oct;204(3):539-50
references.pmid: 5824104
references.citation: Anat Rec. 1998 Apr;253(2):49-57
references.pmid: 9605360
references.citation: J Comput Neurosci. 2008 Dec;25(3):520-42
references.pmid: 18506610
references.citation: J Appl Physiol (1985). 1999 Nov;87(5):1861-76
references.pmid: 10562631
references.citation: J Neurophysiol. 1965 May;28(3):599-620
references.pmid: 5835487
references.citation: J Electromyogr Kinesiol. 1994;4(1):15-26
references.pmid: 20870543
references.citation: Phys Med Biol. 1996 Nov;41(11):2251-69
references.pmid: 8938025
references.citation: Circ Res. 1963 Jan;12:40-50
references.pmid: 13975592
references.citation: Front Physiol. 2012 Sep 13;3:358
references.pmid: 22993509
references.citation: J Appl Physiol (1985). 1996 Dec;81(6):2339-46
references.pmid: 9018476
references.citation: Med Eng Phys. 1999 Oct;21(8):583-8
references.pmid: 10672793
references.citation: Comput Math Methods Med. 2013;2013:174342
references.pmid: 23762179