A novel automated autism spectrum disorder detection system

被引:27
作者
Oh, Shu Lih [1 ]
Jahmunah, V. [1 ]
Arunkumar, N. [2 ]
Abdulhay, Enas W. [3 ]
Gururajan, Raj [4 ]
Adib, Nahrizul [5 ]
Ciaccio, Edward J. [6 ]
Cheong, Kang Hao [7 ]
Acharya, U. Rajendra [1 ,8 ,9 ]
机构
[1] Ngee Ann Polytech, Dept Elect & Comp Engn, Singapore 599489, Singapore
[2] SASTRA Univ, Dept Elect & Instrumentat, Thanjavur, India
[3] Jordan Univ Sci & Technol, Fac Engn, Biomed Engn Dept, Ar Ramtha, Jordan
[4] Univ Southern Queensland, Sch Management & Enterprise, Springfield, Qld, Australia
[5] Univ Malaya, Dept Biomed Engn, Fac Engn, Kuala Lumpur, Malaysia
[6] Columbia Univ, Dept Med, New York, NY USA
[7] Singapore Univ Technol & Design SUTD, Sci Math & Technol Cluster, Singapore 487372, Singapore
[8] Asia Univ, Dept Bioinformat & Med Engn, 500 Lioufeng Rd, Taichung 41354, Taiwan
[9] Kumamoto Univ, Int Res Org Adv Sci & Technol IROAST, Kumamoto, Japan
关键词
Autism; Machine learning; Nonlinear features; SVM polynomial; Tenfold cross-validation; Marginal fisher analysis; Autism diagnosis index; EEG SIGNALS; EARLY-DIAGNOSIS; ENTROPY; BISPECTRUM; FEATURES;
D O I
10.1007/s40747-021-00408-8
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:2399 / 2413
页数:15
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