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...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
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 |
| Summary: | 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. |
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