Abstract
Epilepsy is a 4th prevalent neurological disorder which affects the individuals in all ages around the world. Epilepsy disorder is characterized by the abnormal movements of human muscles, called seizure, as a result of the abnormality in the brain electrical activity. The electroencephalogram (EEG) can serve as a powerful tool for detecting Epilepsy. In this paper, the most commonly used Andrzejak database is utilized for building an automated system for epilepsy detection. Digital Wavelet Transform (DWT) is applied on the segmented EEG signals to extract the five EEG sub-bands (delta, theta, alpha, beta, and gamma). Approximation and Abe entropies along with line length are calculated for the extracted sub-bands. Support Vector Machine (SVM) classifier with Radial Basis Function (RBF) kernel function is used to distinguish between three classes: (1) normal, (2) interictal (seizure free interval), and (3) ictal (during seizure). The best accuracies achieved are 93.75%, 98.75% and 98.125% for normal, interictal and ictal classes respectively. These accuracies are achieved using the combination of both Abe entropy and line length features together.
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Naser, A., Tantawi, M., Shedeed, H., Tolba, M.F. (2020). Detecting Epileptic Seizures Using Abe Entropy, Line Length and SVM Classifier. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_17
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