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Title: Epileptic seizure prediction by non-linear methods

Abstract

This research discloses methods and apparatus for automatically predicting epileptic seizures monitor and analyze brain wave (EEG or MEG) signals. Steps include: acquiring the brain wave data from the patient; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis tools; obtaining time serial trends in the nonlinear measures; comparison of the trend to known seizure predictors; and providing notification that a seizure is forthcoming. 76 figs.

Inventors:
; ; ;
Issue Date:
Research Org.:
Lockheed Martin Energy Syst Inc
Sponsoring Org.:
USDOE, Washington, DC (United States)
OSTI Identifier:
321272
Patent Number(s):
5,857,978
Application Number:
PAN: 8-619,030
Assignee:
Lockheed Martin Energy Systems, Inc., Oak Ridge, TN (United States) PTO; SCA: 550600; PA: EDB-99:031768; SN: 99002064441
DOE Contract Number:  
AC05-84OR21400
Resource Type:
Patent
Resource Relation:
Other Information: PBD: 12 Jan 1999
Country of Publication:
United States
Language:
English
Subject:
55 BIOLOGY AND MEDICINE, BASIC STUDIES; EPILEPSY; FORECASTING; BRAIN; TIME-SERIES ANALYSIS; CALCULATION METHODS

Citation Formats

Hively, L.M., Clapp, N.E., Day, C.S., and Lawkins, W.F. Epileptic seizure prediction by non-linear methods. United States: N. p., 1999. Web.
Hively, L.M., Clapp, N.E., Day, C.S., & Lawkins, W.F. Epileptic seizure prediction by non-linear methods. United States.
Hively, L.M., Clapp, N.E., Day, C.S., and Lawkins, W.F. Tue . "Epileptic seizure prediction by non-linear methods". United States.
@article{osti_321272,
title = {Epileptic seizure prediction by non-linear methods},
author = {Hively, L.M. and Clapp, N.E. and Day, C.S. and Lawkins, W.F.},
abstractNote = {This research discloses methods and apparatus for automatically predicting epileptic seizures monitor and analyze brain wave (EEG or MEG) signals. Steps include: acquiring the brain wave data from the patient; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis tools; obtaining time serial trends in the nonlinear measures; comparison of the trend to known seizure predictors; and providing notification that a seizure is forthcoming. 76 figs.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1999},
month = {1}
}