LSTM-based Electroencephalogram Classification on Autism Spectrum Disorder


  • Nur A. Ali Universiti Teknikal Malaysia Melaka
  • Syafeeza Ahmad Radzi Universiti Teknikal Malaysia Melaka
  • Shukur Jaafar Universiti Teknikal Malaysia Melaka
  • S. Shamsuddin Universiti Teknikal Malaysia Melaka
  • Norazlin Kamal Nor Hospital Censelor Tuanku Mukhriz (HUKM)


Deep learning algorithm, brain signal, lectroencephalogram, autism spectrum disorder


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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How to Cite

A. Ali, N., Ahmad Radzi, S., Jaafar, S., Shamsuddin, S. ., & Kamal Nor, N. (2021). LSTM-based Electroencephalogram Classification on Autism Spectrum Disorder. International Journal of Integrated Engineering, 13(6), 321–329. Retrieved from