Abstract:
This studyaims to analyze and process Electroencephalogram (EEG)signals using an automated classification method with Support vector machine
(SVM), to categorize patient’s seizure: epileptic or non-epileptic.
We employed a framework of signal analysis techniques, and we started by applying discrete wavelet decomposition(DWT) on the original signal, followed by extracting a set of statistical features and building the feature matrix.
Next, a feature reduction PCA and ICA were explored to represent the data in a new distinct space with reduced dimension.
Finally, an SVM algorithm was trained and used upon a set of testing data to be classified: epileptic or not.
The performance of classification process due to different methods is presented and compared to show the excellent classification process.
