Time-varying dimension analysis of EEG using adaptive principal component analysis and model selection

P. Celka, M. Mesbah, M. Keir, B. Boashash, P. Colditz

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper present a new approach to the analysis of non-stationary possibly nonlinear time series. It is based on an adaptive autocorrelation eigenspectrum computation known as APEX together with a model selection rule. New concepts of stochastic instantaneous embedding dimension and time averaged instantaneous embedding dimension are introduced. The motivation for this new approach is the analysis of newborn electroencephalogram for which non-stationarity is a crutial property. Experimental data are analyzed using the proposed scheme.

Original languageEnglish
Pages (from-to)1404-1407
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume2
DOIs
Publication statusPublished - 2000

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Electroencephalography
Principal Component Analysis
Autocorrelation
Principal component analysis
Time series

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

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