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Title: Robust Detection of Dynamical Change in Scalp EEG

Abstract

We present a robust, model-independent technique for measuring changes in the dynamics underlying nonlinear time-serial data. We define indicators of dynamical change by comparing distribution functions on the attractor via L{sub 1}-distance and X{sup 2} statistics. We apply the measures to scalp EEG data with the objective of capturing the transition between non-seizure and epileptic brain activity in a timely, accurate, and non-invasive manner. We find a clear superiority of the new metrics in comparison to traditional nonlinear measures as discriminators of dynamical change.

Authors:
; ;
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (US)
OSTI Identifier:
11469
Report Number(s):
ORNL/CP-103699
TRN: AH200128%%768
DOE Contract Number:  
AC05-96OR22464
Resource Type:
Conference
Resource Relation:
Conference: 5th Experimental Chaos Conference, Orlando, FL (US), 06/28/1999--07/01/1999; Other Information: PBD: 28 Jun 1999
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; ATTRACTORS; BRAIN; DETECTION; DISCRIMINATORS; DISTRIBUTION FUNCTIONS; METRICS; STATISTICS

Citation Formats

Gailey, P C, Hively, L M, and Protopopescu, V A. Robust Detection of Dynamical Change in Scalp EEG. United States: N. p., 1999. Web.
Gailey, P C, Hively, L M, & Protopopescu, V A. Robust Detection of Dynamical Change in Scalp EEG. United States.
Gailey, P C, Hively, L M, and Protopopescu, V A. 1999. "Robust Detection of Dynamical Change in Scalp EEG". United States. https://www.osti.gov/servlets/purl/11469.
@article{osti_11469,
title = {Robust Detection of Dynamical Change in Scalp EEG},
author = {Gailey, P C and Hively, L M and Protopopescu, V A},
abstractNote = {We present a robust, model-independent technique for measuring changes in the dynamics underlying nonlinear time-serial data. We define indicators of dynamical change by comparing distribution functions on the attractor via L{sub 1}-distance and X{sup 2} statistics. We apply the measures to scalp EEG data with the objective of capturing the transition between non-seizure and epileptic brain activity in a timely, accurate, and non-invasive manner. We find a clear superiority of the new metrics in comparison to traditional nonlinear measures as discriminators of dynamical change.},
doi = {},
url = {https://www.osti.gov/biblio/11469}, journal = {},
number = ,
volume = ,
place = {United States},
year = {1999},
month = {6}
}

Conference:
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