Optimization for Nonlinear Time Series and Forecast for Sleep

It is important processes that phase-space diagram and computation of geometrical eigenvalues are reconstituted in nonlinear dynamical analysis. It’s difficult to analyze nonlinear system such as EEG real-time because the algorithms of phase-space diagram reconstitution and geometrical eigenvalue co...

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Published inLife System Modeling and Intelligent Computing pp. 597 - 603
Main Authors Shao, Chenxi, He, Xiaoxu, Tong, Songtao, Dou, Huiling, Yang, Ming, Wang, Zicai
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
correlation dimension
forecast
nonlinear system
parallel computation
sleep EEG
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Summary:It is important processes that phase-space diagram and computation of geometrical eigenvalues are reconstituted in nonlinear dynamical analysis. It’s difficult to analyze nonlinear system such as EEG real-time because the algorithms of phase-space diagram reconstitution and geometrical eigenvalue computation are complex on both time and space. The algorithms were optimized to reduce their complexity, after that the algorithms were parallelized, at last the integrated algorithm’s running time is 1/30 of the running time before optimization and parallelization. It was found that the value of correlation dimension can reflect sleep stages after analyzing the sleep EEG, final sleep stages were also forecasted simply.
Bibliography:Supported by Key Project of Natural Science Foundation of China (Grant No. 60874065 and 60434010) and the Science Research Fund of MOE-Microsoft Key Laboratory of Multimedia Computing and Communication (Grant No. 06120803).
ISBN:3642156142
9783642156144
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-15615-1_70