<![CDATA[Epilepsy is the most common neurological disorder in humans characterized by recurrent ictal (convulsions). Ictalism is defined as a sudden change in the electrical function of the brain, resulting in behavioral changes, such as loss of consciousness, jerky movements, loss of breath and temporary memory. Epilepsy is a chronic, non-communicable brain disease that affects about 50 million people worldwide. Electroencephalogram (EEG) signals contain important details regarding the electrical actions performed by the brain. EEG signal analysis is important for detecting diseases, one of which is epilepsy. However, these signals can be complex and require human expertise. The random, non-stationary behavior of the EEG signal makes ictal prediction difficult. So ictal detection and prediction is a very important issue. Various signal processing methods along with feature extraction are adapted to categorize EEG signal segments to obtain specific characteristics of the signal. The purpose of this paper is to improve the accuracy of the classification of epileptic ictal, then the EEG signal feature extraction is carried out so that the specific characteristics of the signal needed in the trial process are obtained. The study data used EEG signals from 24 patients from the CHB-MIT EEG public dataset from Children's Hospital, Boston. Signals were recorded from 23 epileptic children (of which 2 cases were obtained from the same child at 1.5 year intervals). The approach presented in this paper for feature extraction uses the Discrete Wavelet Transform (DWT) method to obtain the characteristics of the EEG signal, while for the classification process using the SVM method. It is hoped that the proposed method can detect epileptic ictal using EEG signal recording.]]>
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