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Modeling the connections of brain regions in children with autism using cellular neural networks and electroencephalography analysis.
Askari, Elham; Setarehdan, Seyed Kamaledin; Sheikhani, Ali; Mohammadi, Mohammad Reza; Teshnehlab, Mohammad.
Afiliación
  • Askari E; Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
  • Setarehdan SK; Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran. Electronic address: ksetareh@ut.ac.ir.
  • Sheikhani A; Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
  • Mohammadi MR; Psychiatry and Psychology Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Teshnehlab M; Department of Control Engineering, K. N. Toosi University of Technology, Tehran, Iran.
Artif Intell Med ; 89: 40-50, 2018 07.
Article en En | MEDLINE | ID: mdl-30007788
The brain connections in the different regions demonstrate the characteristics of brain activities. In addition, in various conditions and with neuropsychological disorders, the brain has special patterns in different regions. This paper presents a model to show and compare the connection patterns in different brain regions of children with autism (53 boys and 36 girls) and control children (61 boys and 33 girls). The model is designed by cellular neural networks and it uses the proper features of electroencephalography. The results show that there are significant differences and abnormalities in the left hemisphere, (p < 0.05) at the electrodes AF3, F3, P7, T7, and O1 in the children with autism compared with the control group. Also, the evaluation of the obtained connections values between brain regions demonstrated that there are more abnormalities in the connectivity of frontal and parietal lobes and the relations of the neighboring regions in children with autism. It is observed that the proposed model is able to distinguish the autistic children from the control subjects with an accuracy rate of 95.1% based on the obtained values of CNN using the SVM method.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Trastorno Autístico / Procesamiento de Señales Asistido por Computador / Encéfalo / Electroencefalografía / Ondas Encefálicas / Modelos Neurológicos / Red Nerviosa Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Child / Female / Humans / Male Idioma: En Revista: Artif Intell Med Asunto de la revista: INFORMATICA MEDICA Año: 2018 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Países Bajos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Trastorno Autístico / Procesamiento de Señales Asistido por Computador / Encéfalo / Electroencefalografía / Ondas Encefálicas / Modelos Neurológicos / Red Nerviosa Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Child / Female / Humans / Male Idioma: En Revista: Artif Intell Med Asunto de la revista: INFORMATICA MEDICA Año: 2018 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Países Bajos