ConVnet BiLSTM for ASD classification on EEG brain signal

Ahmad Radzi, Syafeeza and Ali, Nur Alisa and Ja'afar, Abd Shukur and Kamal Nor, Norazlin (2022) ConVnet BiLSTM for ASD classification on EEG brain signal. International Journal of Online and Biomedical Engineering, 18 (11). pp. 77-94. ISSN 2626-8493

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Abstract

As a neurodevelopmental disability, Autism Spectrum Disorder (ASD) is classified as a spectrum disorder. The availability of an automated technology system to classify the ASD trait would have a significant impact on paediatricians, as it would assist them in diagnosing ASD in children using a quantifiable method. In this paper, we propose a novel autism diagnosis method that is based on a hybrid of the deep learning algorithms. This hybrid consists of a convolutional neural network (ConVnet) architecture that merges two LSTM blocks (BiLSTM) with the other direction of propagation to classify the output state on the brain signal data from electroencephalogram (EEG) on individuals; typically development (TD) and autism (ASD) obtained from the Simon Foundation Autism Research Initiative (SFARI) database to classify the output state. For a 70:30 data distribution, an accuracy of 97.7 percent was achieved. Proposed methods outperformed the current state-of-the art in terms of autism classification efficiency and have the potential to make a significant contribution to neuroscience research, as demonstrated by the results.

Item Type:	Article
Uncontrolled Keywords:	State-of-art architecture, Autism, Classification, Brain signal
Divisions:	Faculty of Electronics and Computer Engineering
Depositing User:	mr eiisaa ahyead
Date Deposited:	28 Feb 2023 07:53
Last Modified:	28 Feb 2023 07:53
URI:	http://eprints.utem.edu.my/id/eprint/26453
Statistic Details:	View Download Statistic

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