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

Journal of computer sciences

article

Zahrul Jannat Peya¹  M. A. H. Akhand¹  Jannatul Ferdous Srabonee¹  Nazmul Siddique² 

[1] Department of Computer Science and Engineering, Khulna University of Engineering &Technology;School of Computing, Engineering and Intelligent Systems, Ulster University

关键词: Autism Spectrum Disorder;    Convolutional Neural Network;    Electroencephalography;    Pearson’s Correlation Coefficient;    Residual Neural Network;   

DOI  :  10.3844/jcssp.2022.695.704

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

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.

【 授权许可】

CC BY