Abstract.
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.
Similar content being viewed by others
Author information
Authors and Affiliations
Additional information
Received: 5 May 1999 / Accepted in revised form: 28 December 1999
Rights and permissions
About this article
Cite this article
Kohlmorgen, J., Müller, KR., Rittweger, J. et al. Identification of nonstationary dynamics in physiological recordings. Biol Cybern 83, 73–84 (2000). https://doi.org/10.1007/s004220000144
Issue Date:
DOI: https://doi.org/10.1007/s004220000144