Epilepsy is a critical neurological disorder with critical influences on the way of living of its victims and prominent features such as persistent convulsion periods followed by unconsciousness. Electroencephalogram (EEG) is one of the commonly used devices for seizure recognition and epilepsy detection. Recognition of convulsions using EEG waves takes a relatively long time because it is conducted physically by epileptologists. The EEG signals are analyzed and categorized, after being captured, into two types, which are normal or abnormal (indicating an epileptic seizure). This study relies on EEG signals which are provided by Arrhythmia Database. Thus, this work is a step beyond the traditional database mission of delivering users’ inquiries; instead, this work is to extract insight and knowledge of such data. The features are extracted from the signals by applying the Discrete Wavelet transform (DWT) method on the input EEG signals. Two different algorithms Support vector machine (SVM) and k-nearest neighbours (KNN) are applied to the extracted features. After using the above method, two different types of EEG are expected by using classification, either to be normal (refers to the normal activeness of the brain) or abnormal (refers to the non-normal activeness of the brain, which may involve epilepsy). The evaluation is based on three parameters (Precision, Recall, and Accuracy), and also on the implementation time. In this research, two different methods are used, the first is the DWT with SVM, and the second is the DWT with KNN. With regard to the three-parameter values and implementation time, it turned out that the second method was more efficient than the first because of its higher accuracy.
