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本文引用的文献

1
A multiscale chemo-electro-mechanical skeletal muscle model to analyze muscle contraction and force generation for different muscle fiber arrangements.一种多尺度化学-电-机械骨骼肌模型,用于分析不同肌纤维排列情况下的肌肉收缩和力产生。
Front Physiol. 2014 Dec 23;5:498. doi: 10.3389/fphys.2014.00498. eCollection 2014.
2
Electromyography and sonomyography analysis of the tibialis anterior: a cross sectional study.经皮电刺激和超声肌图分析胫骨前肌:一项横断面研究。
J Foot Ankle Res. 2014 Feb 8;7(1):11. doi: 10.1186/1757-1146-7-11.
3
Modeling the chemoelectromechanical behavior of skeletal muscle using the parallel open-source software library OpenCMISS.使用并行开源软件库 OpenCMISS 对骨骼肌的机电化学行为进行建模。
Comput Math Methods Med. 2013;2013:517287. doi: 10.1155/2013/517287. Epub 2013 Nov 17.
4
Investigation of innervation zone shift with continuous dynamic muscle contraction.连续动态肌肉收缩时神经支配区移位的研究。
Comput Math Methods Med. 2013;2013:174342. doi: 10.1155/2013/174342. Epub 2013 Jun 3.
5
Volume conductor models in surface electromyography: computational techniques.容积导体模型在表面肌电图中的应用:计算技术。
Comput Biol Med. 2013 Aug 1;43(7):942-52. doi: 10.1016/j.compbiomed.2013.02.002. Epub 2013 Mar 13.
6
A physiologically based, multi-scale model of skeletal muscle structure and function.一种基于生理学的骨骼肌结构与功能多尺度模型。
Front Physiol. 2012 Sep 13;3:358. doi: 10.3389/fphys.2012.00358. eCollection 2012.
7
Decorrelation of cortical inputs and motoneuron output.皮层输入与运动神经元输出的去相关。
J Neurophysiol. 2011 Nov;106(5):2688-97. doi: 10.1152/jn.00336.2011. Epub 2011 Jul 27.
8
OpenCMISS: a multi-physics & multi-scale computational infrastructure for the VPH/Physiome project.OpenCMISS:一个用于 VPH/生理系统项目的多物理和多尺度计算基础架构。
Prog Biophys Mol Biol. 2011 Oct;107(1):32-47. doi: 10.1016/j.pbiomolbio.2011.06.015. Epub 2011 Jul 7.
9
Crosstalk in surface electromyography: Theoretical and practical estimates.表面肌电图中的串扰:理论和实际估计。
J Electromyogr Kinesiol. 1994;4(1):15-26. doi: 10.1016/1050-6411(94)90023-X.
10
Decoding the neural drive to muscles from the surface electromyogram.从表面肌电图解码到肌肉的神经驱动。
Clin Neurophysiol. 2010 Oct;121(10):1616-23. doi: 10.1016/j.clinph.2009.10.040. Epub 2010 May 4.

使用多尺度化学-电-机械有限元模型在实际条件下预测肌电信号。

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

作者信息

Mordhorst Mylena, Heidlauf Thomas, Röhrle Oliver

机构信息

Institute of Applied Mechanics (CE) , University of Stuttgart , Pfaffenwaldring 7, 70569 Stuttgart , Germany ; Stuttgart Research Centre for Simulation Technology , Pfaffenwaldring 5a, 70569 Stuttgart , Germany.

出版信息

Interface Focus. 2015 Apr 6;5(2):20140076. doi: 10.1098/rsfs.2014.0076.

DOI:10.1098/rsfs.2014.0076
PMID:25844148
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4342944/
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.

摘要

本文提出了一种基于多尺度有限元的新型框架,用于对肌电图(EMG)信号进行建模。该框架结合了:(i)半肌节水平上兴奋 - 收缩偶联的生物物理描述;(ii)动作电位(AP)沿肌肉纤维传播的模型;(iii)肌肉力产生和变形的连续介质力学公式;以及(iv)预测肌内和表面肌电图的模型。由于半肌节的生物物理描述,该模型固有地考虑了骨骼肌的生理特性。为了证明这一点,研究了持续收缩期间膜疲劳对肌电图信号的影响。在100Hz的500ms刺激期内,预测的肌电图幅度下降40%,动作电位传播速度下降15%。此外,该模型可以考虑收缩引起的肌肉变形。通过模拟理想化几何形状和人类胫骨前肌(TA)模型的固定长度收缩来证明这一点。TA模型进一步表明,所提出的有限元模型能够模拟实际的几何形状、复杂的纤维结构,并且可以包括不同类型的不均匀性。此外,TA模型考虑了分布式神经支配区、不同的纤维类型,并采用基于α运动神经元生物物理描述的运动单位放电时间。