Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals

The-Hanh Pham, Jahmunah Vicnesh, Joel Koh En Wei, Shu Lih Oh, N. Arunkumar, Enas. W. Abdulhay, Edward J. Ciaccio and U. Rajendra Acharya
Additional contact information
The-Hanh Pham: School of Engineering, Ngee Ann Polytechnic, 535 Clementi Rd, Singapore 599489, Singapore
Jahmunah Vicnesh: School of Engineering, Ngee Ann Polytechnic, 535 Clementi Rd, Singapore 599489, Singapore
Joel Koh En Wei: School of Engineering, Ngee Ann Polytechnic, 535 Clementi Rd, Singapore 599489, Singapore
Shu Lih Oh: School of Engineering, Ngee Ann Polytechnic, 535 Clementi Rd, Singapore 599489, Singapore
N. Arunkumar: Department of Electronics and Instrumentation, SASTRA University, Thirumalaisamudram, Thanjavur 613401, India
Enas. W. Abdulhay: Biomedical Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, P.O.Box 3030, Irbid 22110, Jordan
Edward J. Ciaccio: Department of Medicine – Columbia University New York, 630 W 168th St, New York, NY 10032, USA
U. Rajendra Acharya: School of Engineering, Ngee Ann Polytechnic, 535 Clementi Rd, Singapore 599489, Singapore

IJERPH, 2020, vol. 17, issue 3, 1-15

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.

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 (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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