Article ID: | iaor20072437 |
Country: | Netherlands |
Volume: | 148 |
Issue: | 1 |
Start Page Number: | 227 |
End Page Number: | 250 |
Publication Date: | Nov 2006 |
Journal: | Annals of Operations Research |
Authors: | Pardalos Panos M., Prokopyev Oleg A., Chaovalitwongse Wanpracha Art |
Epilepsy is among the most common brain disorders. Approximately 25–30% of epilepsy patients remain unresponsive to anti-epileptic drug treatment, which is the standard therapy for epilepsy. In this study, we apply optimization-based data mining techniques to classify the brain's normal and epilepsy activity using intracranial electroencephalogram (EEG), which is a tool for evaluating the physiological state of the brain. A statistical cross validation and support vector machines were implemented to classify the brain's normal and abnormal activities. The results of this study indicate that it may be possible to design and develop efficient seizure warning algorithms for diagnostic and therapeutic purposes.