Electroencephalogram (EEG) time series classification: Applications in epilepsy

Wanpracha Chaovalitwongse (), Oleg Prokopyev and Panos Pardalos

Annals of Operations Research, 2006, vol. 148, issue 1, 227-250

Abstract: 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. Copyright Springer Science + Business Media, LLC 2006

Keywords: Classification; EEG; Brain dynamics; Optimization; Epilepsy; Support vector machines (search for similar items in EconPapers)
Date: 2006
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DOI: 10.1007/s10479-006-0076-x

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