Gilchrist J M, Perrone M, Ross J
Department of Clinical Neuroscience, Brown University School of Medicine, Providence, Rhode Island, USA.
Muscle Nerve. 1995 Jul;18(7):685-92. doi: 10.1002/mus.880180703.
Utilizing prolonged axonal stimulation single fiber EMG, neuromuscular transmission becomes a time-series of interpotential intervals (IPIs). In this form, the underlying processes of neuromuscular transmission can be studied using standard numerical techniques to determine whether these processes can be described by a simple mathematical model. In particular, neuromuscular transmission jitter can be examined in this way. In this article, we attempt to determine whether healthy jitter is noise or deterministic chaos. The presence of deterministic chaos was assessed by analysis of the IPI time-series using visual inspection of both phase-space plots and their principal component dimensions, and using the Grassberger-Procaccia algorithm to determine the correlation dimension of the time-series dynamics. These graphical and mathematical techniques provided little evidence for the existence of deterministic chaos. Linear autoregression time-series prediction also failed to account for the variability of the data and IPI histograms exhibited simple gaussian distributions. These results suggest normal neuromuscular transmission jitter is the result of intrinsic noise.
利用延长的轴突刺激单纤维肌电图，神经肌肉传递成为一系列电位间期（IPI）的时间序列。以这种形式，可以使用标准数值技术研究神经肌肉传递的潜在过程，以确定这些过程是否可以用简单的数学模型来描述。特别是，可以通过这种方式检查神经肌肉传递抖动。在本文中，我们试图确定健康的抖动是噪声还是确定性混沌。通过对IPI时间序列进行分析来评估确定性混沌的存在，方法是对相空间图及其主成分维度进行目视检查，并使用格拉斯贝格尔-普罗卡恰算法确定时间序列动力学的关联维数。这些图形和数学技术几乎没有提供确定性混沌存在的证据。线性自回归时间序列预测也无法解释数据的变异性，并且IPI直方图呈现出简单的高斯分布。这些结果表明，正常的神经肌肉传递抖动是内在噪声的结果。
