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IDA
2010
Springer

Deterministic Finite Automata in the Detection of EEG Spikes and Seizures

9 years 6 months ago
Deterministic Finite Automata in the Detection of EEG Spikes and Seizures
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.
Rory A. Lewis, Doron Shmueli, Andrew M. White
Added 12 Aug 2010
Updated 12 Aug 2010
Type Conference
Year 2010
Where IDA
Authors Rory A. Lewis, Doron Shmueli, Andrew M. White
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