Seizure Detection Based on EEG Signals Using Katz Fractal and SVM Classifiers

Epilepsy is a brain disorder characterized by the occurance of seizure. The International League Against Epilepsy describes that the least occurence of seizure is twice in 24 hours. There are more than 50 million people in the world suffering from this disease. Recurring seizures in epilepsy can be seen from changes in a pattern in EEG signals. EEG is one of the common tools that can be used to record brain activity rather than MRI and CT Scan. EEG recording is conducted by placing electrodes in the scalp (sEEG) or inside the cranium (iEEG). EEG signals are non-linear and non-stationary, so it is difficult to interpret them manually. In order to minimize the mistake during manual interpretation, various methods have been developed to analyse EEG signals in epilepsy patients using digital signal processing. In this study, we proposed a fractal-based method to detect normal, pre-ictal, and seizure from EEG signals. First, EEG signals were decomposed into five sub-bands: Alpha, Beta, Theta, Delta, and Gamma. The Katz fractal dimension (KFD) was calculated for each EEG signal sub-band to be used as features. A Support Vector Machine (SVM) with six different kernels was used as the classifier. The highest accuracy of 98.7% was achieved for three classes of EEG signal data.