Seizure prediction and blockage of seizures is a top research priority in epilepsy, a disease affecting about 1% of the US population. Due to the extreme complexity of brain dynamics, its analysis requires sophisticated methodologies and algorithms from applied mathematics, physics, and engineering. Much emphasis has been placed on techniques from nonlinear dynamics, including application of synchronization and dynamical invariants techniques. In this proposal, we will perform a systematic assessment of these techniques for the task of seizure detection and prediction on long time series. First, we look for optimal synchronization measures, examining phase synchronization, generalized synchronization, and unstable periodic orbit analysis, and assessing each for its sensitivity and specificity for prediction or detection of epileptic seizures. We will test these strategies as well Lyapunov exponents on multi-day, continuous ECoG recordings from several patients, with each recording containing several seizures.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
3R01NS046602-03S1
Application #
7448058
Study Section
Special Emphasis Panel (ZRG1-SSS-S (10))
Program Officer
Stewart, Randall R
Project Start
2004-08-01
Project End
2008-06-30
Budget Start
2007-07-01
Budget End
2008-06-30
Support Year
3
Fiscal Year
2007
Total Cost
$45,900
Indirect Cost
Name
Flint Hills Scientific, LLC
Department
Type
DUNS #
023309755
City
Lawrence
State
KS
Country
United States
Zip Code
66049
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Lai, Ying-Cheng; Frei, Mark G; Osorio, Ivan et al. (2007) Characterization of synchrony with applications to epileptic brain signals. Phys Rev Lett 98:108102
Lai, Ying-Cheng; Frei, Mark G; Osorio, Ivan (2006) Detecting and characterizing phase synchronization in nonstationary dynamical systems. Phys Rev E Stat Nonlin Soft Matter Phys 73:026214