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.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE, Washington, DC (United States)
- OSTI Identifier:
- 321272
- Patent Number(s):
- 5857978
- Application Number:
- PAN: 8-619,030
- Assignee:
- Lockheed Martin Energy Systems, Inc., Oak Ridge, TN (United States)
- 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}
}
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