Sensitive Measures of Condition Change in EEG Data
Conference
·
OSTI ID:6563
We present a new, robust, model-independent technique for measuring condition change in nonlinear data. We define indicators of condition change by comparing distribution functions (DF) defined on the attractor for time windowed data sets via L{sub 1}-distance and {chi}{sup 2} statistics. The new measures are applied to EEG data with the objective of detecting the transition between non-seizure and epileptic brain activity in an accurate and timely manner. We find a clear superiority of the new metrics in comparison to traditional nonlinear measures as discriminators of condition change.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Energy Research (ER) (US)
- DOE Contract Number:
- AC05-96OR22464
- OSTI ID:
- 6563
- Report Number(s):
- ORNL/CP-102445; EB 50 03 00 0; EB 50 03 00 0; TRN: AH200116%%243
- Resource Relation:
- Conference: International Conference on Chaos in Brain, Bonn (DE), 03/10/1999--03/12/1999; Other Information: PBD: 10 Mar 1999
- Country of Publication:
- United States
- Language:
- English
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