Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13062-5_11 | IDA |
Keywords | Field | DocType |
deterministic finite automaton,data mining,statistical analysis,signal analysis,eeg epileptic spikes,epileptic seizure,correlation platform,deterministic finite automata,mining eeg pattern,intelligent data analysis,eeg spike,action rules | State transition table,Signal processing,Pattern recognition,Computer science,Deterministic finite automaton,Epileptic seizure,Artificial intelligence,Knowledge extraction,Electroencephalography,Machine learning,Ictal,Statistical analysis | Conference |
Volume | ISSN | ISBN |
6065 | 0302-9743 | 3-642-13061-5 |
Citations | PageRank | References |
6 | 1.04 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rory A. Lewis | 1 | 57 | 10.07 |
| Doron Shmueli | 2 | 6 | 1.04 |
| Andrew M. White | 3 | 154 | 11.33 |