Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals

Pham, The-Hanh; Vicnesh, Jahmunah; Wei, Joel Koh En; Oh, Shu Lih; Arunkumar, N.; Abdulhay, Enas. W.; Ciaccio, Edward J.; Acharya, U. Rajendra

doi:10.3390/ijerph17030971

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Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals

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School of Engineering, Ngee Ann Polytechnic, 535 Clementi Rd, Singapore 599489, Singapore

Department of Electronics and Instrumentation, SASTRA University, Thirumalaisamudram, Thanjavur 613401, India

Biomedical Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, P.O.Box 3030, Irbid 22110, Jordan

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Department of Medicine – Columbia University New York, 630 W 168th St, New York, NY 10032, USA

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Department of Bioinformatics and Medical Engineering, Asia University, 500, Lioufeng Rd., Wufeng, Taichung 41354, Taiwan

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International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, 2-39-1 Kurokami Chuo-ku, Kumamoto 860-855, Japan

Author to whom correspondence should be addressed.

Int. J. Environ. Res. Public Health 2020, 17(3), 971; https://doi.org/10.3390/ijerph17030971

Received: 25 December 2019 / Revised: 29 January 2020 / Accepted: 30 January 2020 / Published: 4 February 2020

(This article belongs to the Section Digital Health)

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Abstract

Autistic individuals often have difficulties expressing or controlling emotions and have poor eye contact, among other symptoms. The prevalence of autism is increasing globally, posing a need to address this concern. Current diagnostic systems have particular limitations; hence, some individuals go undiagnosed or the diagnosis is delayed. In this study, an effective autism diagnostic system using electroencephalogram (EEG) signals, which are generated from electrical activity in the brain, was developed and characterized. The pre-processed signals were converted to two-dimensional images using the higher-order spectra (HOS) bispectrum. Nonlinear features were extracted thereafter, and then reduced using locality sensitivity discriminant analysis (LSDA). Significant features were selected from the condensed feature set using Student’s t-test, and were then input to different classifiers. The probabilistic neural network (PNN) classifier achieved the highest accuracy of 98.70% with just five features. Ten-fold cross-validation was employed to evaluate the performance of the classifier. It was shown that the developed system can be useful as a decision support tool to assist healthcare professionals in diagnosing autism. View Full-Text

Keywords: autism spectrum disorder; computer-aided brain diagnostic system; EEG signals; higher-order spectra bispectrum; nonlinear features; locality sensitivity discriminant analysis; t-test; classifiers; 10-fold validation

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MDPI and ACS Style

Pham, T.-H.; Vicnesh, J.; Wei, J.K.E.; Oh, S.L.; Arunkumar, N.; Abdulhay, E.W.; Ciaccio, E.J.; Acharya, U.R. Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals. Int. J. Environ. Res. Public Health 2020, 17, 971. https://doi.org/10.3390/ijerph17030971

AMA Style

Pham T-H, Vicnesh J, Wei JKE, Oh SL, Arunkumar N, Abdulhay EW, Ciaccio EJ, Acharya UR. Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals. International Journal of Environmental Research and Public Health. 2020; 17(3):971. https://doi.org/10.3390/ijerph17030971

Chicago/Turabian Style

Pham, The-Hanh, Jahmunah Vicnesh, Joel Koh En Wei, Shu Lih Oh, N. Arunkumar, Enas. W. Abdulhay, Edward J. Ciaccio, and U. Rajendra Acharya. 2020. "Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals" International Journal of Environmental Research and Public Health 17, no. 3: 971. https://doi.org/10.3390/ijerph17030971

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