Main Page
Deanship
The Dean
Dean's Word
Curriculum Vitae
Contact the Dean
Vision and Mission
Organizational Structure
Vice- Deanship
Vice- Dean
KAU Graduate Studies
Research Services & Courses
Research Services Unit
Important Research for Society
Deanship's Services
FAQs
Research
Staff Directory
Files
Favorite Websites
Deanship Access Map
Graduate Studies Awards
Deanship's Staff
Staff Directory
Files
Researches
Contact us
عربي
English
About
Admission
Academic
Research and Innovations
University Life
E-Services
Search
Deanship of Graduate Studies
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 Type
Dr Grade
Email
ابتهال علوي السقاف
Alsaggaf, Ebtehal Alawi
Researcher
Master
Files
File Name
Type
Description
35187.pdf
pdf
Back To Researches Page