Characterizing autistic disorder based on principle component analysis
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 th...
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| Format: | Conference or Workshop Item |
| Language: | English |
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2011
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| Online Access: | http://irep.iium.edu.my/21763/ http://irep.iium.edu.my/21763/1/Characterizing_autistic_disorder_based_on_Principle_Component_Analysis.pdf |
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iium-217632012-12-31T11:19:04Z http://irep.iium.edu.my/21763/ Characterizing autistic disorder based on principle component analysis Shams, Wafaa Khazaal Abdul Rahman, Abdul Wahab 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. 2011-09 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/21763/1/Characterizing_autistic_disorder_based_on_Principle_Component_Analysis.pdf Shams, Wafaa Khazaal and Abdul Rahman, Abdul Wahab (2011) Characterizing autistic disorder based on principle component analysis. In: 2011 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2011, 25-28 September 2011, Langkawi, Malaysia. |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| description |
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. |
| format |
Conference or Workshop Item |
| author |
Shams, Wafaa Khazaal Abdul Rahman, Abdul Wahab |
| spellingShingle |
Shams, Wafaa Khazaal Abdul Rahman, Abdul Wahab Characterizing autistic disorder based on principle component analysis |
| author_facet |
Shams, Wafaa Khazaal Abdul Rahman, Abdul Wahab |
| author_sort |
Shams, Wafaa Khazaal |
| title |
Characterizing autistic disorder based on
principle component analysis |
| title_short |
Characterizing autistic disorder based on
principle component analysis |
| title_full |
Characterizing autistic disorder based on
principle component analysis |
| title_fullStr |
Characterizing autistic disorder based on
principle component analysis |
| title_full_unstemmed |
Characterizing autistic disorder based on
principle component analysis |
| title_sort |
characterizing autistic disorder based on
principle component analysis |
| publishDate |
2011 |
| url |
http://irep.iium.edu.my/21763/ http://irep.iium.edu.my/21763/1/Characterizing_autistic_disorder_based_on_Principle_Component_Analysis.pdf |
| first_indexed |
2023-09-18T20:33:12Z |
| last_indexed |
2023-09-18T20:33:12Z |
| _version_ |
1777408873654648832 |