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

Publication ,  Journal Article
Krystal, AD; Prado, R; West, M
Published in: Clin Neurophysiol
December 1999

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.

Duke Scholars

Published In

Clin Neurophysiol

DOI

ISSN

1388-2457

Publication Date

December 1999

Volume

110

Issue

12

Start / End Page

2197 / 2206

Location

Netherlands

Related Subject Headings

  • Time Factors
  • Regression Analysis
  • Neurology & Neurosurgery
  • Models, Neurological
  • Humans
  • Electroencephalography
  • Brain
  • 3209 Neurosciences
  • 17 Psychology and Cognitive Sciences
  • 11 Medical and Health Sciences
 

Citation

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Krystal, A. D., Prado, R., & West, M. (1999). New methods of time series analysis of non-stationary EEG data: eigenstructure decompositions of time varying autoregressions. Clin Neurophysiol, 110(12), 2197–2206. https://doi.org/10.1016/s1388-2457(99)00165-0
Krystal, A. D., R. Prado, and M. West. “New methods of time series analysis of non-stationary EEG data: eigenstructure decompositions of time varying autoregressions.Clin Neurophysiol 110, no. 12 (December 1999): 2197–2206. https://doi.org/10.1016/s1388-2457(99)00165-0.
Krystal, A. D., et al. “New methods of time series analysis of non-stationary EEG data: eigenstructure decompositions of time varying autoregressions.Clin Neurophysiol, vol. 110, no. 12, Dec. 1999, pp. 2197–206. Pubmed, doi:10.1016/s1388-2457(99)00165-0.
Journal cover image

Published In

Clin Neurophysiol

DOI

ISSN

1388-2457

Publication Date

December 1999

Volume

110

Issue

12

Start / End Page

2197 / 2206

Location

Netherlands

Related Subject Headings

  • Time Factors
  • Regression Analysis
  • Neurology & Neurosurgery
  • Models, Neurological
  • Humans
  • Electroencephalography
  • Brain
  • 3209 Neurosciences
  • 17 Psychology and Cognitive Sciences
  • 11 Medical and Health Sciences