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Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals

Author

Listed:
  • 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
    Department of Bioinformatics and Medical Engineering, Asia University, 500, Lioufeng Rd., Wufeng, Taichung 41354, Taiwan
    International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, 2-39-1 Kurokami Chuo-ku, Kumamoto 860-855, Japan)

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.

Suggested Citation

  • The-Hanh Pham & Jahmunah Vicnesh & Joel Koh En Wei & Shu Lih Oh & N. Arunkumar & Enas. W. Abdulhay & Edward J. Ciaccio & U. Rajendra Acharya, 2020. "Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals," IJERPH, MDPI, vol. 17(3), pages 1-15, February.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:3:p:971-:d:316494
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    References listed on IDEAS

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    1. Karatzoglou, Alexandros & Meyer, David & Hornik, Kurt, 2006. "Support Vector Machines in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i09).
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    1. Prabal Datta Barua & Jahmunah Vicnesh & Raj Gururajan & Shu Lih Oh & Elizabeth Palmer & Muhammad Mokhzaini Azizan & Nahrizul Adib Kadri & U. Rajendra Acharya, 2022. "Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review," IJERPH, MDPI, vol. 19(3), pages 1-26, January.
    2. Kiah Evans & Andrew J. O. Whitehouse & Emily D’Arcy & Maya Hayden-Evans & Kerry Wallace & Rebecca Kuzminski & Rebecca Thorpe & Sonya Girdler & Benjamin Milbourn & Sven Bölte & Angela Chamberlain, 2022. "Perceived Support Needs of School-Aged Young People on the Autism Spectrum and Their Caregivers," IJERPH, MDPI, vol. 19(23), pages 1-24, November.
    3. Afshin Shoeibi & Marjane Khodatars & Navid Ghassemi & Mahboobeh Jafari & Parisa Moridian & Roohallah Alizadehsani & Maryam Panahiazar & Fahime Khozeimeh & Assef Zare & Hossein Hosseini-Nejad & Abbas K, 2021. "Epileptic Seizures Detection Using Deep Learning Techniques: A Review," IJERPH, MDPI, vol. 18(11), pages 1-33, May.
    4. Chin-Chuan Shih & Chi-Jie Lu & Gin-Den Chen & Chi-Chang Chang, 2020. "Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals," IJERPH, MDPI, vol. 17(14), pages 1-11, July.
    5. Chi-Chang Chang & Chun-Chia Chen & Chalong Cheewakriangkrai & Ying Chen Chen & Shun-Fa Yang, 2021. "Risk Prediction of Second Primary Endometrial Cancer in Obese Women: A Hospital-Based Cancer Registry Study," IJERPH, MDPI, vol. 18(17), pages 1-9, August.

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