中断刺激下瘫痪四头肌肌力的肌电图和代谢物预测。

O Levin, J Mizrahi
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引用次数: 17

摘要

与功能性电刺激(FES)相关的一个主要问题是由于疲劳导致肌肉力量随时间的衰减。在FES过程中减少疲劳的一种可能方法是使用中断刺激,其中疲劳和恢复依次发生。在这项研究中,我们提出了一个模型,使我们能够评估电激活肌肉在第一次刺激疲劳期间的时间力产生能力,即当肌肉从非疲劳初始状态激活时,以及在应激后刺激期间,即在不同的给定休息时间后。肌肉的力史由实际测量的肌电图(EMG)数据得出的激活、表达肌肉代谢物时间变化的代谢疲劳函数、体内31P MR光谱获得的无机磷变量Pi或H2PO4-的现有数据以及细胞内ph来确定。该模型在等长收缩中被休息期分开的超极大刺激得到求解。并与实验测量结果进行比较。肌电图数据是预测破伤风后上升力的基础。另一方面,只能通过基于代谢产物的疲劳函数来预测力的衰减阶段。通过力剖面预测值与实测值之间的误差,对模型的预测能力进行了评价。该模型预测的首次刺激疲劳受力与试验结果吻合较好。在应激后疲劳中,不同的代谢产物提供了不同的力预测能力,这取决于休息时间的长短。静息1 min后,Pi最能预测力;H2PO4-将模型的预测能力扩展到6分钟,pH对超过12分钟的休息时间提供了可靠的预测。研究结果揭示了肌电图和代谢物在预测疲劳和恢复依次发生的瘫痪肌肉的力史中的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EMG and metabolite-based prediction of force in paralyzed quadriceps muscle under interrupted stimulation.

A major issue associated with functional electrical stimulation (FES) of a paralyzed limb is the decay with time of the muscle force as a result of fatigue. A possible means to reduce fatigue during FES is by using interrupted stimulation, in which fatigue and recovery occur in sequence. In this study, we present a model which enables us to evaluate the temporal force generation capacity within the electrically activated muscle during first stimulation fatigue, i.e., when the muscle is activated from unfatigued initial conditions, and during postrest stimulation, i.e., after different given rest durations. The force history of the muscle is determined by the activation as derived from actually measured electromyogram (EMG) data, and by the metabolic fatigue function expressing the temporal changes of muscle metabolites, from existing data acquired by in vivo 31P MR spectroscopy in terms of the inorganic phosphorus variables, Pi or H2PO4-, and by the intracellular pH. The model was solved for supra-maximal stimulation in isometric contractions separated by rest periods, and compared to experimentally obtained measurements. EMG data were fundamental for prediction of the ascending force during its posttetanic response. On the other hand, prediction of the decaying phase of the force was possible only by means of the metabolite-based fatigue function. The prediction capability of the model was assessed by means of the error between predicted and measured force profiles. The predicted force obtained from the model in first stimulation fatigue fits well with the experimental one. In postrest stimulation fatigue, the different metabolites provided different prediction capabilities of the force, depending on the duration of the rest period. Following rest duration of 1 min, Pi provided the best prediction of force; H2PO4- extended the prediction capacity of the model to up to 6 min and pH provided a reliable prediction for rest durations longer than 12 min. The results presented shed light on the roles of EMG and of metabolites in prediction of the force history of a paralyzed muscle under conditions where fatigue and recovery occur in sequence.

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