• DocumentCode
    1195429
  • Title

    Discerning nonstationarity from nonlinearity in seizure-free and preseizure EEG recordings from epilepsy patients

  • Author

    Rieke, Christoph ; Mormann, Florian ; Andrzejak, Ralph G. ; Kreuz, Thomas ; David, Peter ; Elger, Christian E. ; Lehnertz, Klaus

  • Author_Institution
    Dept. of Epileptology, Univ. of Bonn, Germany
  • Volume
    50
  • Issue
    5
  • fYear
    2003
  • fDate
    5/1/2003 12:00:00 AM
  • Firstpage
    634
  • Lastpage
    639
  • Abstract
    A number of recent studies indicate that nonlinear electroencephalogram (EEG) analyses allow one to define a state predictive of an impending epileptic seizure. In this paper, we combine a method for detecting nonlinear determinism with a novel test for stationarity to characterize EEG recordings from both the seizure-free interval and the preseizure phase. We discuss differences between these periods, particularly an increased occurrence of stationary, nonlinear segments prior to seizures. These differences seem most prominent for recording sites within the seizure-generating area and for EEG segments less than one minute´s length.
  • Keywords
    brain models; diseases; electroencephalography; medical signal detection; nonlinear dynamical systems; prediction theory; time series; EEG recordings; EEG segments; epilepsy patients; impending epileptic seizure; nonlinear determinism; nonlinear electroencephalogram analyses; nonlinearity; nonstationarity; preseizure EEG recordings; seizure-free EEG recordings; seizure-generating area; stationary nonlinear segments; Diseases; Disk recording; Electroencephalography; Epilepsy; Neurons; Nuclear physics; Phase detection; Testing; Time measurement; Time series analysis; Electroencephalography; Epilepsies, Partial; Humans; Nonlinear Dynamics; Reproducibility of Results; Retrospective Studies; Seizures; Sensitivity and Specificity; Stochastic Processes;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/TBME.2003.810684
  • Filename
    1198253