Epileptic Seizure Detection System Using Multi-Channel EEG as Basis for Classification
Date Issued
2012
Date
2012
Author(s)
Liu, Shih-Ting
Abstract
Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Seizure episodes can cause temporal paralysis of the body, which can lead to severe injuries. Electroencephalogram (EEG) is a tool commonly used for analyzing brain activity and diagnosing brain disorders. EEG can be presented under different montage schemes. This study focuses on two of the montage schemes; unipolar montage and bipolar montage. Traditionally, the most commonly used montage for automated EEG analysis is unipolar. We experiment with incorporating bipolar EEG montage for creating a classification system to classify different epileptic wave forms. A series of functions were designed for bipolar EEG montage. We used wavelet transform (WT) to decompose EEG signal into its primary sub-bands. We use Approximate Entropy and Total Variation as features designed specifically for spike and seizure detection. We used Genetic Algorithm and Fisher Score to rank and selected most influential features for classifier. Finally we use multi-class Support Vector Machine as our classifier.
Subjects
Genetic Algorithm
Fisher Score
Support Vector Machines
Type
thesis
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