Document Details

Document Type : Thesis 
Document Title :
DISCOVERING AUTISM DISORDER BY ANALYSIS EEG SIGNALS USING DIFFERENT CLASSIFICATION ALGORITHMS
اكتشاف اضطراب التوحد من خلال تحليل إشارات التخطيط الدماغي باستخدام خوارزميات التصنيف المختلفة
 
Subject : Faculty of Computing and Information Technology - Computing Sciences 
Document Language : Arabic 
Abstract : Diagnosis of autism is one of the difficult problems that researchers and those interested in the field of special education and medicine are facing. Therefore, there is a lot of research going on around the world today trying to use Neuroscience to treat and diagnose children with ASD. Hence, there is arising need for using neuroscience with computer science to diagnose autistic people. In this research, appropriate classification algorithms will be used to extract the appropriate features and to classify the EEG signals in order to discriminate between autistic and normal children. In this thesis, we studied the optimum preprocessing, as well as optimum features extraction, which give the highest classification accuracy between normal and autistic children. This new approach will achieve a better medical diagnosis, discover early children with the disorder and help the parents to reduce the time and the human errors of using traditional diagnosis process. This research is considered as part of the main BCI project in the King AbdulAziz University that is funded by (King AbdulAziz City for Science and Technology) KACST, 8-NAN106-3. 
Supervisor : Dr. Mahmoud Ibrahim Kamel Ali 
Thesis Type : Master Thesis 
Publishing Year : 1434 AH
2013 AD 
Co-Supervisor : Dr. Hussein Muhammad Ahmed Malibary 
Added Date : Monday, March 11, 2013 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
ابتهال علوي السقافAlsaggaf, Ebtehal AlawiResearcherMaster 

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