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Characterizing autistic disorder based on principle component analysis

Shams, Wafa Khazal and Abdul Rahman, Abdul Wahab (2012) Characterizing autistic disorder based on principle component analysis. Australian Journal of Basic and Applied Sciences, 6 (1). pp. 149-155. ISSN 1991-8178

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Abstract

Autism is often diagnosed during preschool or toddled age. This diagnosis often depends on behavioral test. It is known that individuals with autism have abnormal brain signals different from typical persons yet this difference in signals is slight that it is often difficult to distinguish from the normal. However, Electroencephalogram (EEG) signals have a lot of information which reflect the behavior of brain functions which therefore captures the marker for autism, help to early diagnose and speed the treatment. This work investigates and compares classification process for autism in open eyed tasks and motor movement by using Principle Component Analysis (PCA) for feature extracted in Time-frequency domain to reduce data dimension. The results show that the proposed method gives accuracy in the range 90-100% for autism and normal children in motor task and around 90% to detect normal in open-eyed tasks though difficult to detect autism in this task.

Item Type: Article (Journal)
Additional Information: 6145/22986
Uncontrolled Keywords: EEG, PCA, autism
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Information and Communication Technology
Kulliyyah of Information and Communication Technology

Kulliyyah of Information and Communication Technology > Department of Computer Science
Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User: Prof Abdul Wahab Abdul Rahman
Date Deposited: 15 Jun 2012 10:34
Last Modified: 15 Jun 2012 10:35
URI: http://irep.iium.edu.my/id/eprint/22986

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