| Identification of nonstationary dynamics in physiological recordings. | |
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MedLine Citation:
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PMID: 10933239 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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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. |
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Authors:
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J Kohlmorgen; K R Müller; J Rittweger; K Pawelzik |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: Biological cybernetics Volume: 83 ISSN: 0340-1200 ISO Abbreviation: Biol Cybern Publication Date: 2000 Jul |
Date Detail:
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Created Date: 2000-11-09 Completed Date: 2000-12-07 Revised Date: 2006-11-15 |
Medline Journal Info:
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Nlm Unique ID: 7502533 Medline TA: Biol Cybern Country: GERMANY |
Other Details:
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Languages: eng Pagination: 73-84 Citation Subset: IM |
Affiliation:
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GMD FIRST, Berlin, Germany. jek@first.gmd.de |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Electroencephalography* Humans Markov Chains Models, Neurological* Respiration Sleep Stages / physiology |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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