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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.

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。

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