Non-linear analysis of EEG signals at various sleep stages
Non-linear analysis of EEG signals at various sleep stages
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DOI：
10.1016/j.cmpb.2005.06.011
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发表时间：
2005-10-01
影响因子：
6.1
通讯作者：
Laxminarayan, S
中科院分区：
文献类型：
--
作者：
Acharya, R;Faust, O;Laxminarayan, S
非线性动力学方法在生理科学中的应用表明，非线性模型有助于理解复杂的生理现象，如突变和混沌行为。睡眠阶段和自主功能的持续波动，例如温度、血压、脑电图（EEG）等，可以说是一个混沌的过程。EEG信号是高度主观的，并且关于各种状态的信息可能在时间尺度上随机出现。因此，使用计算机提取和分析的EEG信号参数在诊断中非常有用。睡眠数据分析使用非线性参数：关联维数，分形维数，最大李雅普诺夫熵，近似熵，赫斯特指数，相空间图和递归图。这些非线性参数量化了不同睡眠阶段的皮质功能，并将结果制成表格。(c)2005爱思唯尔爱尔兰有限公司保留所有权利。
Application of non-linear dynamics methods to the physiological sciences demonstrated that non-linear models are useful for understanding complex physiological phenomena such as abrupt transitions and chaotic behavior. Sleep stages and sustained fluctuations of autonomic functions such as temperature, blood pressure, electroencephalogram (EEG), etc., can be described as a chaotic process. The EEG signals are highly subjective and the information about the various states may appear at random in the time scale. Therefore, EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. The sleep data analysis is carried out using non-linear parameters: correlation dimension, fractal dimension, largest Lyapunov entropy, approximate entropy, Hurst exponent, phase space plot and recurrence plots. These non-linear parameters quantify the cortical function at different sleep stages and the results are tabulated. (c) 2005 Elsevier Ireland Ltd. All rights reserved.