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One of the most common brain disorders is epilepsy. A person who has epilepsy is not able to have normal days like the others. It’s characterized by more than two unprovoked seizures. However, the faster detection and treatment of epileptic seizures, the quicker reduction of the disease abnormal level. Neurologists are still diagnosing, detecting, and testing a seizure manually by observing the Electroencephalogram (EEG) signals. This takes a very long time because of the irregularity of EEG signals. Hence, a Computer-Aided Diagnosis (CAD) is developed by many scientists to help neurologists in detecting seizures automatically. In this research, a CAD system was developed at CHB-MIT dataset. The EEG signals were processed at several stages through this system, namely pre-processing, decomposition, feature extraction, and classification. In pre-processing, the EEG signals were uniformed by selecting the most appropriate channels and filtered using Butterworth Bandpass Filter (BPF) to remove noise. The process continued to the decomposition and feature extraction stage using Empirical Mode Decomposition (EMD) and fractal dimension-based methods, i.e. Higuchi, Katz, and Sevcik, respectively. Then, the features were classified by Support Vector Machine (SVM). The proposed method achieved the highest accuracy at 94.72% on the Chb07 record. Meanwhile, the average accuracy was 81.2% for all records. The proposed study is expected to be applied for the detection of seizure onset in a real-time system.

Epilepsy is the most common form of neurological disease. Patients with epilepsy may experience seizures of a certain duration with or without provocation. Epilepsy analysis can be done with an electroencephalogram (EEG) examination. Observation of qualitative EEG signals generates high cost and often confuses due to the nature of the non-linear EEG signal and noise. In this study, we proposed an EEG signal processing system for EEG seizure detection. The signal dynamics approach to normal and seizure signals' characterization became the main focus of this study. Spectral Entropy (SpecEn) and fractal analysis are used to estimate the EEG signal dynamics and used as feature sets. The proposed method is validated using a public EEG dataset, which included preictal, ictal, and interictal stages using the Naïve Bayes classifier. The test results showed that the proposed method is able to generate an ictal detection accuracy of up to 100%. It is hoped that the proposed method can be considered in the detection of seizure signals on the long-term EEG recording. Thus it can simplify the diagnosis of epilepsy.

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