Kim Y W, Krieble K K, Kim C B, Reed J, Rae-Grant A D
Department of Physics, Lehigh University, Bethlehem, Pennsylvania 18015, USA.
Electroencephalogr Clin Neurophysiol. 1996 Jan;98(1):35-41. doi: 10.1016/0013-4694(95)00186-7.
The electroencephalogram, as a probe of scalp-recorded electrical activity arising from the human cortex, provides useful information because of its temporal and spatial organization. Recent developments in nonlinear dynamics suggest that an object can be constructed in an n-dimensional space out of a temporal sequence of data such as an EEG signal and that its organization is characterized by the dimensionality of the object (in this case, human brain activity). We have carried out an analysis of a set of alpha coma EEG patterns in comparison to the awake alpha EEG patterns of normal volunteers and patients. Alpha coma recorded from a single channel is visually indistinguishable from normal resting alpha due to its similar frequency spectrum (a broad-band spectrum with 1/f characteristics). Our results show that alpha coma dimensionality, however, differs from that of normal alpha in that it has a greater variability over different temporal segments of EEG. Single channel recordings in 7 patients with alpha coma were differentiable from those of 10 subjects with "normal" EEGs. Through dynamic analysis of the EEG, novel methods of signal extraction from EEG may become evident and applicable to clinical practice.
脑电图作为探测源自人类皮质的头皮记录电活动的手段，因其时间和空间组织特性而能提供有用信息。非线性动力学的最新进展表明，可以根据诸如脑电图信号这样的数据时间序列在n维空间中构建一个对象，并且其组织特征由该对象的维度（在这种情况下，即人类大脑活动）来表征。我们对一组α昏迷脑电图模式进行了分析，并与正常志愿者和患者的清醒α脑电图模式进行了比较。从单通道记录的α昏迷由于其相似的频谱（具有1/f特征的宽带频谱），在视觉上与正常静息α无法区分。然而，我们的结果表明，α昏迷的维度与正常α的维度不同，因为它在脑电图的不同时间段具有更大的变异性。7例α昏迷患者的单通道记录与10例脑电图“正常”受试者的记录是可区分的。通过对脑电图的动态分析，从脑电图中提取信号的新方法可能会变得明显并适用于临床实践。
