A neuroimaging study in childhood autism

Mullick, Mohammad S. I.; Rahman, Wasima; Alim, SM Abu Hena Mostafa; Chowdhury, Hafizur Rahman

doi:10.3329/bsmmuj.v6i2.29127

Cited by 2 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The leave one out cross-validation of the characterization calculation gives 94.7% precision as the best execution with relating affectability and explicitness esteems as 85.7 and 100% individually. Mullick et al (2013) conducted a cross-sectional analysis to determine the proportion of sMRI and EEG abnormalities in autistic children, as well as any connection between MRI and EEG changes and comorbid mental illness. A total of 42 autistic children between the ages of two and twelve participated in the study at a child and adolescent consultation center.…”

Section: Literature Reviewmentioning

confidence: 99%

Autism Detection from 2D Transformed EEG Signal using Convolutional Neural Network

Peya¹,

Akhand²,

Srabonee³

et al. 2022

Journal of Computer Science

View full text Add to dashboard Cite

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder relating to speech complications, nonverbal and social communication, and repetitive behaviors. There is no remedy for ASD but early diagnosis, mediation, and supportive care can aid the development of language, conduct, and communication skills. As the cause of ASD is a neurodevelopmental disorder, its diagnosis based on brain function analyzing different brain signals, especially Electroencephalography (EEG), has drawn attention recently. Brain activity is recorded over time as an EEG signal from the scalp of a human and is used to investigate complicated neuropsychiatric disorders in the brain. In this study, the data from the EEG channels are translated into two-Dimensional (2D) form through correlation, and classification is performed using Convolutional Neural Networks (CNN), the well-known deep learning method for image analysis and classification. Two different CNN models are considered for classification purposes: Generic CNN and Residual Network (ResNet), a well-known deep CNN model. The proposed method with Resnet achieved 88% classification accuracy on a five-fold cross-validation mode, whereas it was 100 on 20% of test samples. Experimental evaluations using clinical EEG data revealed the efficacy of the proposed method outperforming other existing methods.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Autism Detection from 2D Transformed EEG Signal using Convolutional Neural Network

Peya¹,

Akhand²,

Srabonee³

et al. 2022

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…1 Neurological disorder shows abnormal EEG and MRI. 2 It can be classified into three subgroups: Autistic disorder (autism), Asperger disorder, and pervasive developmental disorder. 3 But the later version DSM-5 collapsed these three into one single broad diagnosis of ASD.…”

Section: Introductionmentioning

confidence: 99%

Role of environmental and genetic factors in autism spectrum disorder

Sultana

Uddin

Ahmed

2017

Bangabandhu Sheikh Mujib Medical University Journal

View full text Add to dashboard Cite

<p>The aim of this study was to find out the environmental as well as genetic factors responsible for increasing the number of autism spectrum disorder (ASD) patients in Bangladesh. A questionnaire was developed based on 12 environmental factors and genetic aspects. Sixty six patients of ASD and 66 non-ASD control were selected randomly. Among the environmental factors, the age of the mother, premature birth, air pollution, age of the father, hypoxia during childbirth and oral contraceptive came out as significant (p<0.05) factors for ASD incidence compared to the control. Association of multiple factors on an individual was found to be crucial to enhance the risk and exposure to five and six factors was statistically significant (p<0.05) for ASD development. Prospective parents should try to keep the number of risk factors as low as possible before 1-2 months of pregnancy, during pregnancy and 1-2 years after the child birth (for child only).</p>

show abstract

A neuroimaging study in childhood autism

Abstract: Abstract:Background: Childhood autism is now widely viewed as being of developmental neurological origin. Abnormality in

Cited by 2 publications

References 20 publications

Autism Detection from 2D Transformed EEG Signal using Convolutional Neural Network

Autism Detection from 2D Transformed EEG Signal using Convolutional Neural Network

Role of environmental and genetic factors in autism spectrum disorder

Contact Info

Product

Resources

About