Kohlmorgen J, Müller K R, Rittweger J, Pawelzik K
GMD FIRST, Berlin, Germany.
Biol Cybern. 2000 Jul;83(1):73-84. doi: 10.1007/s004220000144.
We present a novel framework for the analysis of time series from dynamical systems that alternate between different operating modes. The method simultaneously segments and identifies the dynamical modes by using predictive models. In extension to previous approaches, it allows an identification of smooth transition between successive modes. The method can be used for analysis, diagnosis, prediction, and control. In an application to EEG and respiratory data recorded from humans during afternoon naps, the obtained segmentations of the data agree with the sleep stage segmentation of a medical expert to a large extent. However, in contrast to the manual segmentation, our method does not require a priori knowledge about physiology. Moreover, it has a high temporal resolution and reveals previously unclassified details of the transitions. In particular, a parameter is found that is potentially helpful for vigilance monitoring. We expect that the method will generally be useful for the analysis of nonstationary dynamical systems, which are abundant in medicine, chemistry, biology and engineering.
我们提出了一个用于分析来自在不同运行模式之间交替的动态系统的时间序列的新框架。该方法通过使用预测模型同时对动态模式进行分割和识别。与先前的方法相比，它允许识别连续模式之间的平滑过渡。该方法可用于分析、诊断、预测和控制。在对人类午睡期间记录的脑电图和呼吸数据的应用中，获得的数据分割在很大程度上与医学专家的睡眠阶段分割一致。然而，与手动分割不同，我们的方法不需要关于生理学的先验知识。此外，它具有高时间分辨率，并揭示了先前未分类的过渡细节。特别是，发现了一个可能有助于警觉性监测的参数。我们预计该方法通常将对非平稳动态系统的分析有用，这些系统在医学、化学、生物学和工程中大量存在。
