Faculty of Information Technology, Pázmány Péter Catholic University, Budapest, Hungary.
J Neurosci Methods. 2009 Dec 15;185(1):116-24. doi: 10.1016/j.jneumeth.2009.07.027. Epub 2009 Jul 29.
Fractality is a common property in nature. It can also be observed in time series representing dynamics of complex processes. Therefore fractal analysis could be a useful tool to describe the dynamics of brain electrical activities in physiological and pathological conditions. In this study, we carried out a spatio-temporal analysis of monofractal and multifractal properties of whole-night sleep EEG recordings. We estimated the Hurst exponent (H) and the range of fractal spectra (dD) in 10 healthy subjects. We found higher H values during NREM4 compared to NREM2 and REM in all electrodes. Measure dD showed an opposite trend. Differences of H and dD between NREM2 and REM reached significancy at circumscribed regions only. Our results contribute to a deeper understanding of the fractal nature of brain electrical activities and may have implications for automatic classification of sleep stages.
分形是自然界的普遍属性。它也可以在代表复杂过程动态的时间序列中观察到。因此，分形分析可能是描述生理和病理条件下脑电活动动态的有用工具。在这项研究中，我们对整个夜间睡眠 EEG 记录的单分形和多分形特性进行了时空分析。我们在 10 名健康受试者中估计了赫斯特指数 (H) 和分形谱范围 (dD)。我们发现，在所有电极中，NREM4 期间的 H 值高于 NREM2 和 REM。测量的 dD 显示出相反的趋势。NREM2 和 REM 之间的 H 和 dD 差异仅在特定区域达到显著水平。我们的结果有助于更深入地了解脑电活动的分形性质，并且可能对睡眠阶段的自动分类具有意义。
