LSTM-based Electroencephalogram Classification on Autism Spectrum Disorder

被引：11

作者：

Ali, N. A. ^{[1
]}

Syafeeza, A. R. ^{[2
]}

Jaafar, A. S. ^{[2
]}

Shamsuddin, S. ^{[3
]}

Nor, Norazlin Kamal ^{[4
]}

机构：

[1] Univ Tekn Malaysia Melaka UTeM, Fak Teknol Kejuruteraan Elekt Elekt FTKEE, Ayer Keroh 75450, Melaka, Malaysia

[2] Univ Tekn Malaysia Melaka UTeM, Fak Kejuruteraan Elekt Kejuruteraan Komputer FKEK, Durian Tunggal 76100, Melaka, Malaysia

[3] Univ Tekn Malaysia Melaka UTeM, Fak Kejuruteraan Pembuatan FKP, Durian Tunggal 76100, Melaka, Malaysia

[4] Hosp Censelor Tuanku Mukhriz HUKM, Fac Med, Dept Paediat, Kuala Lumpur 56000, Malaysia

来源：

INTERNATIONAL JOURNAL OF INTEGRATED ENGINEERING | 2021年 / 13卷 / 06期

关键词：

Deep learning algorithm; brain signal; electroencephalogram; autism spectrum disorder; CHILDREN; BRAIN;

D O I：

10.30880/ijie.13.06.028

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：321 / 329

页数：9

共 33 条

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