Modeling the connections of brain regions in children with autism using cellular neural networks and electroencephalography analysis

Elham Askari; Seyed Kamaledin Setarehdan; Ali Sheikhani; Mohammad Reza Mohammadi; Mohammad Teshnehlab

doi:10.1016/j.artmed.2018.05.003

Modeling the connections of brain regions in children with autism using cellular neural networks and electroencephalography analysis

Artif Intell Med. 2018 Jul:89:40-50. doi: 10.1016/j.artmed.2018.05.003. Epub 2018 Jul 11.

Authors

Elham Askari¹, Seyed Kamaledin Setarehdan², Ali Sheikhani³, Mohammad Reza Mohammadi⁴, Mohammad Teshnehlab⁵

Affiliations

¹ Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
² 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.
³ Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
⁴ Psychiatry and Psychology Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran.
⁵ Department of Control Engineering, K. N. Toosi University of Technology, Tehran, Iran.

PMID: 30007788
DOI: 10.1016/j.artmed.2018.05.003

Abstract

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.

Keywords: Autism; Cellular neural network; Electroencephalography; Emotiv Epoch.

Publication types

Validation Study

MeSH terms

Age Factors
Algorithms
Autistic Disorder / diagnosis*
Autistic Disorder / physiopathology
Brain / physiopathology*
Brain Waves*
Case-Control Studies
Child
Electroencephalography / methods*
Female
Humans
Male
Models, Neurological*
Nerve Net / physiopathology*
Predictive Value of Tests
Reproducibility of Results
Signal Processing, Computer-Assisted*
Wavelet Analysis