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整夜睡眠脑电图和人工随机控制信号具有相似的关联维数。

All-night sleep EEG and artificial stochastic control signals have similar correlation dimensions.

作者信息

Achermann P, Hartmann R, Gunzinger A, Guggenbühl W, Borbély A A

机构信息

Institute of Pharmacology, University of Zurich, Switzerland.

出版信息

Electroencephalogr Clin Neurophysiol. 1994 May;90(5):384-7. doi: 10.1016/0013-4694(94)90054-x.

Abstract

EEG signals have been considered to be generated either by stochastic processes or by non-linear deterministic systems exhibiting chaotic behavior. To address this problem, the correlation dimension of the EEG was computed and compared to the correlation dimension of an artificial signal with identical power spectrum. By using a new type of personal super computer we were able for the first time to calculate the correlation dimension for the sleep episode of an entire night as well as for the corresponding artificial signal. The correlation dimension was high in episodes of rapid eye movement (REM) sleep, declined progressively within each non-REM sleep episode, and reached a low level at times when EEG slow waves (0.75-4.5 Hz) were dominant. The correlation dimension of the artificial signal and the EEG changed largely in parallel, although on average the values of the artificial signal were 7.3% higher. These results do not support the hypothesis that the sleep EEG is generated by a chaotic attractor.

摘要

脑电图(EEG)信号被认为是由随机过程或表现出混沌行为的非线性确定性系统产生的。为了解决这个问题,计算了脑电图的关联维数,并将其与具有相同功率谱的人工信号的关联维数进行比较。通过使用一种新型的个人超级计算机,我们首次能够计算出整个夜晚睡眠阶段以及相应人工信号的关联维数。快速眼动(REM)睡眠阶段的关联维数较高,在每个非快速眼动睡眠阶段中逐渐下降,并在脑电图慢波(0.75 - 4.5赫兹)占主导时达到较低水平。人工信号和脑电图的关联维数在很大程度上并行变化,尽管人工信号的值平均高出7.3%。这些结果不支持睡眠脑电图由混沌吸引子产生的假设。

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