Signal Modeling and Simulation of Temporal Dispersion and Conduction Block in Motor Nerves
Signal Modeling and Simulation of Temporal Dispersion and Conduction Block in Motor Nerves
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DOI：
10.1109/tbme.2019.2954592
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发表时间：
2020-07-01
影响因子：
4.6
通讯作者：
Schmidt, Gerhard
中科院分区：
文献类型：
--
作者：
Elzenheimer, Eric;Laufs, Helmut;Schmidt, Gerhard
目的：神经电图是一种成熟的诊断试验，用于支持有髓周围神经疾病的诊断。自动计算神经生理学量，并用于确定神经（轴突损伤）或其鞘（髓鞘损伤）的病理学。具体的鉴别诊断标准来自时域规范性数据，其主要来自20世纪90年代初基于动物数据（即大鼠）的计算机模拟。然而，由于解剖学上的差异，研究的大鼠信号与人类的信号有显着差异。方法：我们提出了一个基于模型的模拟神经传导在健康和病理运动神经。与早期的模拟相比，本模型是基于从真实的人体测量中提取的运动单元动作电位，从传导速度分布开始，促进真实信号的生成。除了健康神经的建模之外，我们还对遗传性周围神经疾病以及急性和慢性炎性脱髓鞘疾病进行建模。结果：基于标准变量的时域定量信号差异。脱髓鞘疾病的研究结果表明，远端和近端反应之间的幅度降低了71%和65%，这是由于神经纤维传导速度的方差增加。结论：模拟结果与经验测量结果非常吻合，表明信号模型捕获了相关的病理机制。在脱髓鞘条件下超过50%的振幅降低与常规测量一致，并且表明与先前的模拟模型相比，时间分散是相当好地建模的。重要性：模拟结果可以作为改善周围神经疾病的病理生理学理解的基础，并应帮助神经生理学家完善他们的诊断armamentarium导致更精确的鉴别诊断。
Objective: Electroneurography is a well-established diagnostic test for supporting the diagnosis of disorders of myelinated peripheral nerves. Neurophysiological quantities are automatically calculated and are used to determine the pathology of the nerve (axonal damage) or its sheath (myelin damage). Specific differential diagnostic criteria are derived from time-domain normative data, which result primarily from a computer simulation in the early 1990s based on animal data, namely rats. However, the rat signals studied differ significantly from those of humans because of anatomical differences. Methods: We present a model-based simulation of nerve conduction in healthy and pathological motor nerves. In contrast to earlier simulations, the present model is based on motor unit action potentials extracted from real human measurements facilitating the generation of realistic signals, starting from a conduction velocity distribution. In addition to the modeling of healthy nerves, we model a hereditary peripheral nerve disease as well as an acute and a chronic inflammatory demyelinating condition. Results: Quantitative signal differences based on standard variables in the time-domain are presented. The findings for the demyelinating conditions demonstrate amplitude reductions of 71% and 65% between the distal and proximal responses, which result from an increase in the variance of the nerve fiber conduction velocities. Conclusion: The simulation results closely match those of empirical measurements, indicating that the signal model captures relevant pathological mechanisms. An amplitude reduction of more than 50% in demyelinating conditions is in accordance with routine measurements and shows that temporal dispersion is quite well-modeled compared to previous simulation models. Significance: The simulation outcomes can serve as the basis for an improved pathophysiological understanding of peripheral nerve disorders and should aid neurophysiologists to refine their diagnostic armamentarium resulting in a more precise differential diagnosis.