LSTM-based electroencephalogram classification on autism spectrum disorder. The International Journal of Integrated Engineering, 13 (6). pp. 321-329. ISSN 2229-838X (2021)
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 |
|---|---|
| Keywords: | Deep learning algorithm, Brain signal, Electroencephalogram, Autism spectrum disorder |
| Taxonomy: | By Subject > College of Engineering > Electrical Engineering > Computer By Subject > College of Engineering > Electrical Engineering > Systems |
| Local Content Hub: | Subjects > College of Engineering |
| Depositing User: | Eza Eliana Abdul Wahid |
| Date Deposited: | 06 Oct 2022 07:28 |
| Last Modified: | 06 Oct 2022 09:26 |
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