Ning Yan, Jiang Zhaohui, An Bin, Feng Huanqing
Department of Electronic Sciences & Technology, University of Sciences & Technology of China, Hefei 230026, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2007 Apr;24(2):249-52.
Detrended fluctuation analysis (DFA) is fit for studies on the long-range exponential correlations of non-stationary time serial. In this paper, for elucidating the characteristics of different sleep stages, DFA is adopted to analyze the physiological data collected during sleep. The parameters such as electroencephalogram (EEG), R-R interval sequence and stroke volume (SV) are analyzed, and the scaling exponent a is calculated. The experimental results reveal that the values of a differ much in different sleep stages,that the rules of EEG and SV are alike, that alpha increases with the deepening of sleep, but in inverse for R-R interval sequence that alpha decreases with the deepening of sleep. These indicate that the method of DFA is practical in the analysis of physiological parameters.
去趋势波动分析(DFA)适用于对非平稳时间序列的长程指数相关性进行研究。本文为阐明不同睡眠阶段的特征,采用DFA分析睡眠期间采集的生理数据。对脑电图(EEG)、R-R间期序列和每搏输出量(SV)等参数进行分析,并计算标度指数α。实验结果表明,α值在不同睡眠阶段差异很大,EEG和SV的规律相似,α随睡眠加深而增大,但R-R间期序列则相反,α随睡眠加深而减小。这些表明DFA方法在生理参数分析中是实用的。