LSTM-based electroencephalogram classification on autism spectrum disorder

Ahmad Radzi, Syafeeza and Ali, Nur Alisa and Ja'afar, Abd Shukur and Shamsuddin, Syamimi and Kamal Nor, Norazlin (2021) LSTM-based electroencephalogram classification on autism spectrum disorder. International Journal of Integrated Engineering, 13 (6). pp. 321-329. ISSN 2229-838X

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Abstract

Abstract: Autism Spectrum Disorder (ASD) is categorized as a neurodevelopmental disability. Having an automated technology system to classify the ASD trait would have a huge influence on paediatricians, which can aid them in diagnosing ASD in children using a quantifiable method. A novel autism diagnosis method based on a bidirectional long-short-term-memory (LSTM) network's deep learning algorithm is proposed. This multi-layered architecture merges two LSTM blocks with the other direction of propagation to classify the output state on the brain signal data from an electroencephalogram (EEG) on individuals; normal and autism obtained from the Simon Foundation Autism Research Initiative (SFARI) database. The accuracy of 99.6% obtained for 90:10 train:test data distribution, while the accuracy of 97.3% was achieved for 70:30 distribution. The result shows that the proposed approach had better autism classification with upgraded efficiency compared to single LSTM network method and potentially giving a significant contribution in neuroscience research.

Item Type:	Article
Uncontrolled Keywords:	Autism spectrum disorder, Brain signal, Deep learning algorithm, Electroencephalogram
Divisions:	Faculty of Electronics and Computer Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	18 Apr 2022 11:42
Last Modified:	18 Apr 2022 11:42
URI:	http://eprints.utem.edu.my/id/eprint/25893
Statistic Details:	View Download Statistic

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