THE INFLUENCE OF NONSTATIONARITY AND SEGMENTATION SIZE ON THE ANALYSIS OF INTRACRANIAL EEG RECORDINGS
Abstract number :
1.135
Submission category :
Year :
2002
Submission ID :
3563
Source :
www.aesnet.org
Presentation date :
12/7/2002 12:00:00 AM
Published date :
Dec 1, 2002, 06:00 AM
Authors :
Christoph Rieke, Ralph G. Andrzejak, Florian Mormann, Thomas Kreuz, Peter David, Christian E. Elger, Klaus Lehnertz. Dept. of Epileptology, University of Bonn, Bonn, Germany; Inst. for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany; John
RATIONALE: Previous studies have demonstrated the relevance of a number of nonlinear time series analysis techniques for the spatiotemporal characterization of the epileptogenic process. Almost all of these techniques require the system under investigation to be stationary. For the dynamical system brain, however, this is far from being the case. EEG recordings of several tens of seconds length, nevertheless are usually regarded as approximately stationary. On longer observation times (segmentation size) of the EEG, however, the statistical significance of an analysis technique should be improved almost always. We here investigate an enlarged observation time of EEG segments up to minutes. We compare the distribution and thus the significance of nonlinear measures of the EEG, covering different states for different observation times. Furthermore, we estimate the nonstationarity of the observed EEG segments and exclude all segments which are significantly nonstationary.
METHODS: EEG segments recorded intracranially in patients with focal epilepsies and covering different states: interictal, preictal, ictal and postictal were analyzed. The EEG segments were enlarged starting with 23.6 s up to 94.4 s corresponding to 4096 data points and 16384 data points respectively. Short segments were included in longer ones.
Analysis techniques comprised nonlinear measures for complexity, determinism and nonlinearity, using iterative amplitude adjusted surrogate data for each segment. Nonstationarity was quantified by measuring the loss of recurrence in reconstructed state space.
RESULTS: Some epochs within long nonstationary segments appear stationary. On the other hand, even segments which are nonstationary appear stationary when they are enlarged.
For most measures, the distributions of the estimated values show a deviation between EEG segments from preictal and interictal states. This deviation enlarged with increasing observation time, particularly for measures employing surrogate data.
CONCLUSIONS: Results suggest that most measures show an increased performance in characterizing and discriminating EEG time series under control of stationarity if the observation time was enlarged.
Moreover, we hypothize that investigating nonstationarity at characteristic time scales might improve the understanding of the spatiotemporal ictogenic process.
[Supported by: Deutsche Forschungsgemeinschaft]