LSTM-based electroencephalogram classification on autism spectrum disorder

• Main Authors: Ahmad Radzi, Syafeeza, Ali, Nur Alisa, Ja'afar, Abd Shukur, Shamsuddin, Syamimi, Kamal Nor, Norazlin
• Format: Article
• Language: English
• Published: Penerbit UTHM 2021
• 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
• Institution: Universiti Teknikal Malaysia Melaka

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.