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Deterministic Finite Automata in the Detection of EEG Spikes and Seizures

  • Rory A. Lewis
  • Doron Shmueli
  • Andrew M. White
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6065)

Abstract

This Paper presents a platform to mine epileptiform activity from Electroencephalograms (EEG) by combining the methodologies of Deterministic Finite Automata (DFA) and Knowledge Discovery in Data Mining (KDD) TV-Tree. Mining EEG patterns in human brain dynamics is complex yet necessary for identifying and predicting the transient events that occur before and during epileptic seizures. We believe that an intelligent data analysis of mining EEG Epileptic Spikes can be combined with statistical analysis, signal analysis or KDD to create systems that intelligently choose when to invoke one or more of the aforementioned arts and correctly predict when a person will have a seizure. Herein, we present a correlation platform for using DFA and Action Rules in predicting which interictal spikes within noise are predictors of the clinical onset of a seizure.

Keywords

Transition Matrix Epileptic Seizure Input State Epileptiform Activity Transition Table 
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-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Rory A. Lewis
    • 1
    • 2
  • Doron Shmueli
    • 1
  • Andrew M. White
    • 1
  1. 1.Departments of Pediatrics & NeurologyUniversity of Colorado DenverDenver
  2. 2.Department of Computer ScienceUniversity of Colorado at Colorado SpringsColorado Springs

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