Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

Evaluation and Comparison of EEG Traces: Latent Structure in Nonstationary Time Series

Mike West, Raquel Prado and Andrew D. Krystal
Journal of the American Statistical Association
Vol. 94, No. 448 (Dec., 1999), pp. 1083-1095
DOI: 10.2307/2669922
Stable URL: http://www.jstor.org/stable/2669922
Page Count: 13
  • Download ($14.00)
  • Cite this Item
Evaluation and Comparison of EEG Traces: Latent Structure in Nonstationary Time Series
Preview not available

Abstract

We explore and illustrate the use of time series decomposition methods for evaluating and comparing latent structure in nonstationary electroencephalographic (EEG) traces obtained from depressed patients during brain seizures induced as part of electroconvulsive therapy (ECT). Analysis of the patterns of change over time in the frequency structure of such EEG data provides insight into the neurophysiological mechanisms of action of this effective but poorly understood antidepressant treatment, and allows clinicians to modify ECT treatments to optimize therapeutic benefits while minimizing associated side effects. Our work has introduced new methods of time-frequency analysis of EEG series that identify the complete pattern of time evolution of frequency structure over the course of a seizure, and usefully assist in these scientific and clinical studies. New methods of decomposition of flexible dynamic models provide time domain decompositions of individual EEG series into collections of latent components in different frequency bands. This allows us to explore ECT seizure characteristics via inferences on the time-varying parameters that characterize these latent components, and to relate differences in such characteristics across seizures to differences in the therapeutic effectiveness and cognitive side effects of those seizures. This article discusses the scientific context and problems, development of nonstationary time series models and new methods of decomposition to explore time-frequency structure, and aspects of model fitting and analysis. We include applied studies on two datasets from recent clinical ECT studies. One is an initial illustrative analysis of a single EEG trace, the second compares the EEG data recorded during two types of ECT treatment that differ in therapeutic effectiveness and cognitive side effects. The uses of these models and time series decomposition methods in extracting and contrasting key features of the seizure underlying the EEG signals are highlighted. Through the use of these models we have quantified, for the first time, decreases in the dominant frequencies of low-frequency EEG components during ECT seizures. We have also identified preliminary evidence that such decreases are enhanced under the more effective ECTs at higher electrical dosages, a finding consistent with prior reports and the hypothesis that more effective forms of ECT are more effective in eliciting neurophysiological inhibitory processes.

Page Thumbnails

  • Thumbnail: Page 
1083
    1083
  • Thumbnail: Page 
1084
    1084
  • Thumbnail: Page 
1085
    1085
  • Thumbnail: Page 
1086
    1086
  • Thumbnail: Page 
1087
    1087
  • Thumbnail: Page 
1088
    1088
  • Thumbnail: Page 
1089
    1089
  • Thumbnail: Page 
1090
    1090
  • Thumbnail: Page 
1091
    1091
  • Thumbnail: Page 
1092
    1092
  • Thumbnail: Page 
1093
    1093
  • Thumbnail: Page 
1094
    1094
  • Thumbnail: Page 
1095
    1095