Abstract
Epileptic seizures are the outcome of the transient and the sudden electrical disorder of the brain. The Electroencephalogram (EEG) is a diagnostic imaging method, which measures the electrical activity in the brain. The main principle of this study is to observe the performance of classifiers regarding Support Vector Machine (SVM) and Artificial Neural Network (ANN) using wavelet coefficients for seizure detection. This paper uses discrete wavelet transform to analyse EEG signals, which are non–stationary. The EEG signals were decomposed by db1, db2 (daubechies wavelet) and haar wavelet. Grey Level Cooccurrence Matrix (GLCM) and statistical features are extracted from the decomposed EEG signal. This work concludes that SVM classifiers using db2 with hybrid features are the best outcomes for EEG signal classification.
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