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Title: Nonlinear analysis of EEG for epileptic seizures

Abstract

We apply chaotic time series analysis (CTSA) to human electroencephalogram (EEG) data. Three epoches were examined: epileptic seizure, non-seizure, and transition from non-seizure to seizure. The CTSA tools were applied to four forms of these data: raw EEG data (e-data), artifact data (f-data) via application of a quadratic zero-phase filter of the raw data, artifact-filtered data (g- data) and that was the residual after subtracting f-data from e-data, and a low-pass-filtered version (h-data) of g-data. Two different seizures were analyzed for the same patient. Several nonlinear measures uniquely indicate an epileptic seizure in both cases, including an abrupt decrease in the time per wave cycle in f-data, an abrupt increase in the Kolmogorov entropy and in the correlation dimension for e-h data, and an abrupt increase in the correlation dimension for e-h data. The transition from normal to seizure state also is characterized by distinctly different trends in the nonlinear measures for each seizure and may be potential seizure predictors for this patient. Surrogate analysis of e-data shows that statistically significant nonlinear structure is present during the non-seizure, transition , and seizure epoches.

Authors:
; ; ;  [1];  [2]
  1. Oak Ridge National Lab., TN (United States)
  2. Knoxville Neurology Clinic, St. Mary`s Medical Center, Knoxville, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE, Washington, DC (United States)
OSTI Identifier:
366563
Report Number(s):
ORNL/TM-12961
ON: DE96013818
DOE Contract Number:  
AC05-96OR22464
Resource Type:
Technical Report
Resource Relation:
Other Information: PBD: Apr 1995
Country of Publication:
United States
Language:
English
Subject:
55 BIOLOGY AND MEDICINE, BASIC STUDIES; 99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; EPILEPSY; ELECTROENCEPHALOGRAPHY; EXPERIMENTAL DATA; BRAIN; TIME-SERIES ANALYSIS; DATA ANALYSIS; NONLINEAR PROBLEMS; STATISTICAL DATA; STATISTICAL MECHANICS

Citation Formats

Hively, L M, Clapp, N E, Daw, C S, Lawkins, W F, and Eisenstadt, M L. Nonlinear analysis of EEG for epileptic seizures. United States: N. p., 1995. Web. doi:10.2172/366563.
Hively, L M, Clapp, N E, Daw, C S, Lawkins, W F, & Eisenstadt, M L. Nonlinear analysis of EEG for epileptic seizures. United States. https://doi.org/10.2172/366563
Hively, L M, Clapp, N E, Daw, C S, Lawkins, W F, and Eisenstadt, M L. 1995. "Nonlinear analysis of EEG for epileptic seizures". United States. https://doi.org/10.2172/366563. https://www.osti.gov/servlets/purl/366563.
@article{osti_366563,
title = {Nonlinear analysis of EEG for epileptic seizures},
author = {Hively, L M and Clapp, N E and Daw, C S and Lawkins, W F and Eisenstadt, M L},
abstractNote = {We apply chaotic time series analysis (CTSA) to human electroencephalogram (EEG) data. Three epoches were examined: epileptic seizure, non-seizure, and transition from non-seizure to seizure. The CTSA tools were applied to four forms of these data: raw EEG data (e-data), artifact data (f-data) via application of a quadratic zero-phase filter of the raw data, artifact-filtered data (g- data) and that was the residual after subtracting f-data from e-data, and a low-pass-filtered version (h-data) of g-data. Two different seizures were analyzed for the same patient. Several nonlinear measures uniquely indicate an epileptic seizure in both cases, including an abrupt decrease in the time per wave cycle in f-data, an abrupt increase in the Kolmogorov entropy and in the correlation dimension for e-h data, and an abrupt increase in the correlation dimension for e-h data. The transition from normal to seizure state also is characterized by distinctly different trends in the nonlinear measures for each seizure and may be potential seizure predictors for this patient. Surrogate analysis of e-data shows that statistically significant nonlinear structure is present during the non-seizure, transition , and seizure epoches.},
doi = {10.2172/366563},
url = {https://www.osti.gov/biblio/366563}, journal = {},
number = ,
volume = ,
place = {United States},
year = {1995},
month = {4}
}