LSTM-based electroencephalogram classification on autism spectrum disorder
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 diag...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit UTHM
2021
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| Online Access: | http://eprints.utem.edu.my/id/eprint/25893/2/8165-ARTICLE%20TEXT-40152-1-10-20210914.PDF http://eprints.utem.edu.my/id/eprint/25893/ https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/8165/4431 |
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| Institution: | Universiti Teknikal Malaysia Melaka |
| Language: | English |
| Summary: | 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. |
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