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神经复合动作电位的线性模型对单纤维动作电位波形的依赖性。

Dependence of the linear model for the nerve compound action potential on the single fibre action potential waveform.

作者信息

Contento G, Barbina V, Malisan M R, Padovani R, Budai R, Pittaro I

出版信息

Clin Phys Physiol Meas. 1983 Nov;4(4):417-33. doi: 10.1088/0143-0815/4/4/006.

DOI:10.1088/0143-0815/4/4/006
PMID:6653045
Abstract

The linear model of the nerve compound action potential (CAP) depends on the assumed waveform for the single fibre action potential (SFAP). A general method has been developed to investigate the influence of the unknown features of the SFAP on the estimation of nerve fibre conduction velocity (CV) distribution. A SFAP waveform is considered consistent with the model and the experimental data if recorded and reconstructed CAPS fit and the distribution is physically meaningful. Experimental CAPS were monopolarly recorded using surface electrodes over the median nerve at the wrist. To fit the model, SFAP waveforms must satisfy some internal relationship. The most important feature is that the ratio between positive and negative areas of the SFAP is almost one and does not vary in different subjects and recording sites. Many SFAP waveforms fit the model, and the relative conduction velocity distributions may be very different. These must be regarded as conventional distributions. As for inter-subject comparison, the dependence of the method on the recording site has been reduced by choosing the place where stimulus intensity and relative motor response amplitude have given values. In this recording environment CV distributions of normal subjects can be properly compared using the same SFAP and deviations from normality evidenced.

摘要

神经复合动作电位(CAP)的线性模型取决于单纤维动作电位(SFAP)的假定波形。已开发出一种通用方法来研究SFAP未知特征对神经纤维传导速度(CV)分布估计的影响。如果记录和重建的CAP拟合且分布具有物理意义,则认为SFAP波形与模型和实验数据一致。实验性CAP是使用手腕处正中神经上方的表面电极进行单极记录的。为了拟合模型,SFAP波形必须满足一些内部关系。最重要的特征是SFAP正负面积之比几乎为1,且在不同受试者和记录部位不会变化。许多SFAP波形都符合该模型,但其相对传导速度分布可能非常不同。这些必须被视为传统分布。至于受试者间比较,通过选择刺激强度和相对运动反应幅度具有给定值的位置,该方法对记录部位的依赖性已降低。在这种记录环境下,可以使用相同的SFAP对正常受试者的CV分布进行适当比较,并证明其与正常情况的偏差。

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