LSTM-based Electroencephalogram Classification on Autism Spectrum Disorder

• Published in: The International Journal of Integrated Engineering Vol. 13; no. 6
• Main Authors: Ali, N. A., Syafeeza, A. R, Jaafar, A. S., Shamsuddin, S., Kamal Nor, Norazlin
• Format: Journal Article
• Language: English
• Published: 01.01.2021
• ISSN: 2229-838X; 2600-7916
• DOI: 10.30880/ijie.13.06.028

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 datafrom 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