Asmaa Hamad Elsaied Mohamed <br/><br/>
Application of swarm intelligence optimization for enhancing detection of epileptic seizures in EEG signals /  
 تطبيق امثلية الذكاء السربي لتحسين نوبات اكتشاف الصرع في إشارات رسم المخ  
Asmaa Hamad Elsaied Mohamed ; Supervised Aly Aly Fahmy , Aboulella Hassanien , Essam Halim Houssein 
 - Cairo :   Asmaa Hamad Elsaied Mohamed ,   2018 
 - 89 Leaves :   charts ;   30cm  
<br/><br/>Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Computer Science 
<br/><br/> The thesis introduces a hybrid classification model using swarm optimization algorithms and support vector machines (SVMs) for automatic seizure detection in EEG. This proposed classification model consists of four main phases; namely,1) EEG pre-processing used to remove the noises from the EEG signals and decompose EEG signal into various sub-bands,2) feature extraction used to extract the EEG signal features from decomposed signal,3) Feature selection and classifier Parameters Optimization based swarm algorithms and 4) classification phase that is mainly used to analyze and classify the EEG signal into normal or abnormal 
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<br/><br/><label>Subjects--Index Terms: </label>EEG Machine Learning Swarm Intelligence