Pilgram B, Schappacher W, Löscher W N, Pfurtscheller G
LaRC Centro di Bioingegneria, Fondazione Pro Juventute and Politecnico di Milano, Italy.
Biol Cybern. 1995;72(6):543-51. doi: 10.1007/BF00199897.
Non-linear time sequence analysis has been performed on infant sleep measurement data in order to obtain more information about the respiratory processes. As a first step, respiration data during REM sleep were analysed with methods from non-linear dynamics, especially, the correlation integral and the slope of its log-log plot, representing the correlation dimension. Before calculation of the correlation integral, a special kind of filtering has to be applied to the data. This filtering algorithm is a state space and singular value decomposition-based noise reduction method, and it is used to separate the noise and signal subspaces. The dynamics of a signal (in our case data from the respiratory process) and its degrees of freedom can be characterised by the correlation integral and by the correlation dimension, respectively. The main result of this study is that the highly irregular-looking breathing patterns during REM sleep could be described by a deterministic system, and finally the physiological significance of this finding is discussed.
为了获取更多关于呼吸过程的信息，已对婴儿睡眠测量数据进行了非线性时间序列分析。第一步，采用非线性动力学方法分析快速眼动睡眠期间的呼吸数据，特别是相关积分及其对数-对数图的斜率，该斜率代表相关维数。在计算相关积分之前，必须对数据应用一种特殊的滤波。这种滤波算法是一种基于状态空间和奇异值分解的降噪方法，用于分离噪声子空间和信号子空间。信号的动力学（在我们的案例中是呼吸过程的数据）及其自由度可分别通过相关积分和相关维数来表征。本研究的主要结果是，快速眼动睡眠期间看似高度不规则的呼吸模式可用确定性系统来描述，最后讨论了这一发现的生理学意义。
