New methods of time series analysis of non-stationary EEG data: eigenstructure decompositions of time varying autoregressions.

Published

Journal Article

OBJECTIVE: Those who analyze EEG data require quantitative techniques that can be validly applied to time series exhibiting ranges of non-stationary behavior. Our objective is to introduce a new analysis technique based on formal non-stationary time series models. This novel method provides a decomposition of the time series into a set of 'latent' components with time-varying frequency content. The identification of these components can lead to practical insights and quantitative comparisons of changes in frequency structure over time in EEG time series. METHODS: The technique begins with the development of time-varying autoregressive models of the EEG time series. Such models have been previously used in EEG analysis but we extend their utility by the introduction of eigenstructure decomposition methods. We review the basis and implementation of this method and report on the analysis of two channel EEG data recorded during 3 generalized tonic-clonic seizures induced in an individual as part of a course of electroconvulsive therapy for major depression. RESULTS: This technique identified EEG patterns consistent with prior reports. In addition, it quantified a decrease in dominant frequency content over the seizures and suggested for the first time that this decrease is continuous across the end of the seizures. The analysis also suggested that the seizure EEG may be best modeled by the combination of multiple processes, whereas post-ictally there appears to be one dominant process. There was also preliminary evidence that these features may differ as a function of ECT therapeutic effectiveness. CONCLUSIONS: Eigenanalysis of time-varying autoregressive models has promise for improving the analysis of EEG time series.

Full Text

Duke Authors

Cited Authors

  • Krystal, AD; Prado, R; West, M

Published Date

  • December 1999

Published In

Volume / Issue

  • 110 / 12

Start / End Page

  • 2197 - 2206

PubMed ID

  • 10616127

Pubmed Central ID

  • 10616127

Electronic International Standard Serial Number (EISSN)

  • 1872-8952

International Standard Serial Number (ISSN)

  • 1388-2457

Digital Object Identifier (DOI)

  • 10.1016/s1388-2457(99)00165-0

Language

  • eng