Electroencephalogram (EEG) time series classification: Applications in epilepsy

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 epi...

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Bibliographic Details
Published inAnnals of operations research Vol. 148; no. 1; pp. 227 - 250
Main Authors Chaovalitwongse, Wanpracha Art, Prokopyev, Oleg A., Pardalos, Panos M.
Format Journal Article
LanguageEnglish
Published New York Springer Nature B.V 01.11.2006
Subjects
Algorithms
Brain research
Chaos theory
Convulsions & seizures
Data mining
Electrodes
Electroencephalography
Epilepsy
Medical diagnosis
Medical treatment
Operations research
Optimization techniques
Patients
Physiology
Studies
Support vector machines
Surgery
Systems engineering
Time series
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ISSN0254-5330
1572-9338
DOI10.1007/s10479-006-0076-x

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Summary: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. [PUBLICATION ABSTRACT]
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-006-0076-x