Deterministic Finite Automata in the Detection of EEG Spikes and Seizures

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6065)


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.


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

Authors and Affiliations

  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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