A novel automated autism spectrum disorder detection system
Article
Oh, Shu Lih, Jahmunah, V., Arunkumar, N., Abdulhay, Enas W., Gururajan, Raj, Kadri, Nahrizul, Ciaccio, Edward J., Cheong, Kang Hao and Acharya, U. Rajendra. 2021. "A novel automated autism spectrum disorder detection system." Complex and Intelligent Systems. 7 (5), pp. 2399-2413. https://doi.org/10.1007/s40747-021-00408-8
| Article Title | A novel automated autism spectrum disorder detection system |
|---|---|
| ERA Journal ID | 212133 |
| Article Category | Article |
| Authors | Oh, Shu Lih, Jahmunah, V., Arunkumar, N., Abdulhay, Enas W., Gururajan, Raj, Kadri, Nahrizul, Ciaccio, Edward J., Cheong, Kang Hao and Acharya, U. Rajendra |
| Journal Title | Complex and Intelligent Systems |
| Journal Citation | 7 (5), pp. 2399-2413 |
| Number of Pages | 15 |
| Year | 2021 |
| Publisher | SpringerOpen |
| Place of Publication | Germany |
| ISSN | 2198-6053 |
| 2199-4536 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s40747-021-00408-8 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s40747-021-00408-8 |
| Abstract | Autism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features. |
| Keywords | Autism; Nonlinear features ; SVM polynomial; Tenfold cross-validation; Marginal fsher analysis; Machine learning; Autism diagnosis index |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Ngee Ann Polytechnic, Singapore |
| SASTRA University, India | |
| Jordan University of Science and Technology, Jordan | |
| School of Management and Enterprise | |
| University of Malaya, Malaysia | |
| Columbia University, United States | |
| Singapore University of Technology and Design | |
| Asia University, Taiwan | |
| Kumamoto University, Japan |
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