Higuchi and Katz Fractal Dimension for Detecting Interictal and Ictal State in Electroencephalogram Signal

Abstract

Epilepsy is a neurological disorder which may occur in every human being. The existence of a seizure showed as the characteristic of this disorder. International League Against Epilepsy (ILAE) mentioned that the diagnose of epilepsy required a minimum of two seizure event in 24 hours. A standard tool used by the neurologist to diagnose epilepsy was Electroencephalogram (EEG). An automatic seizure detection in EEG signal may help them to identify the pattern of ictal condition. Since the characteristic of the EEG signal were dynamic and nonstationary, it was very challenging to interpret the signal pattern. In this study, we analyzed the use of Higuchi and Katz fractal dimension as a feature extraction method to detect an interictal and ictal state in the EEG signal. These two states were essential in the term of seizure detection and prediction system. EEG signal extracted into five frequency bands called delta, theta, alpha, beta, and gamma. Each frequency showed a different characteristic of brain-behavior in a specific condition. The extracted features then fed into a support vector machine (SVM) to classify between normal with interictal and ictal states. The proposed method able to determine normal vs. ictal state with 100% of accuracy. On the other hand, the best accuracy obtained for detecting normal vs. interictal state was 99.5%.