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Dynamical Analysis of the EEG and Treatment of Human Status Epilepticus by Antiepileptic Drugs

  • Aaron Faith
  • Shivkumar Sabesan
  • Norman Wang
  • David Treiman
  • Joseph Sirven
  • Konstantinos Tsakalis
  • Leon Iasemidis
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 38)

Abstract

An estimated 42,000 epileptic patients die from status epilepticus (SE) every year in the United States alone. Evaluation of antiepileptic drugs and protocols for SE treatment, in terms of the dynamics of concurrently monitored electroencephalogram (EEG), may lead to the design of new, more effective treatment paradigms for successfully controlling SE. Such monitoring techniques may have a profound effect in the treatment of SE in the emergency department (ED) and intensive care unit (ICU), where antiepileptic drugs (AEDs) are given in rapid succession in the hope of patient recovery, or even in the epilepsy monitoring unit (EMU), where occasionally a patient may progress to SE. In the past, using techniques from nonlinear dynamics and synchronization theory, we have shown that successful treatment with AEDs results in dynamical disentrainment (desynchronization) of entrained brain sites in SE, a phenomenon we have called dynamical resetting. We herein apply this nonlinear dynamical analysis to scalp EEG recordings from two patients, one admitted to the EMU and the other to the ED and ICU and both treated with AEDs, to show that successful administration of AEDs dynamically disentrains the brain and correlates well with the patients’ recovery. This result further supports our hypothesis of dynamical resetting of the brain by AEDs into the recovery regime, and indicates that the proposed measures/methodology may assist in an objective evaluation of the efficacy of current and the design of future AEDs for the treatment of SE.

Keywords

Lyapunov Exponent Status Epilepticus Electrode Site Brain Dynamic Brain Site 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.The Harrington Department of BioengineeringArizona State UniversityTempeUSA
  2. 2.Barrow Neurological InstitutePhoenixUSA
  3. 3.Mayo ClinicPhoenixUSA
  4. 4.Department of Electrical EngineeringArizona State UniversityTempeUSA

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