Fuzzy model for detection and estimation of the degree of autism spectrum disorder

Shams, Wafaa Khazaal and Abdul Rahman, Abdul Wahab and A. Qidwai, Uvais (2012) Fuzzy model for detection and estimation of the degree of autism spectrum disorder. In: Proceedings of the 19th International Conference on Neural Information Processing (ICONIP 2012), November 12-15, 2012, Doha, Qatar.

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Abstract

Early detection of autism spectrum disorder (ASD) is of great significance for early intervention. Besides, knowing the degree of severity in ASD and how it changes with the intervention is imperative for the treatment process. This study proposes Takagi- Sugeno-Kang (TSK) fuzzy modeling approach that is based on subtractive clustering to classify autism spectrum disorder and to estimate the degree of prognosis. The study has been carried out using Electroencephalography (EEG) signal on two groups of control and ASD children age-matched between seven to nine years old. EEG signals are quantized to temporal-time domain using Short Time Frequency Transformation (STFT). Spectrum energy is extracted as features for alpha band. The proposed system is modeled to estimate the degree in which subject is autistic, normal or uncertain. The results show accuracy in range (70-97) % when using fuzzy model .Also this system is modeled to generate crisp decision; the results show accuracy in the range (80-100) %. The proposed model can be adapted to help psychiatrist for diagnosis and intervention process.

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Additional Information:	6145/29062 doi>10.1007/978-3-642-34478-7_46
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 > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Yusnizar Fuad
Date Deposited:	15 Feb 2013 15:35
Last Modified:	17 Dec 2020 00:52
URI:	http://irep.iium.edu.my/id/eprint/29062

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