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Electroencephalogram (EEG) for Delineating Objective Measure of Autism Spectrum Disorder

Electroencephalogram (EEG) for Delineating Objective Measure of Autism Spectrum Disorder
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Author(s): Sampath Jayarathna (Old Dominion University, USA), Yasith Jayawardana (Old Dominion University, USA), Mark Jaime (Indiana University-Purdue University Columbus, USA) and Sashi Thapaliya (California State Polytechnic University – Pomona, USA)
Copyright: 2019
Pages: 32
Source title: Computational Models for Biomedical Reasoning and Problem Solving
Source Author(s)/Editor(s): Chung-Hao Chen (Old Dominion University, USA) and Sen-Ching Samson Cheung (University of Kentucky, USA)
DOI: 10.4018/978-1-5225-7467-5.ch002

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Abstract

Autism spectrum disorder (ASD) is a developmental disorder that often impairs a child's normal development of the brain. According to CDC, it is estimated that 1 in 6 children in the US suffer from development disorders, and 1 in 68 children in the US suffer from ASD. This condition has a negative impact on a person's ability to hear, socialize, and communicate. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. EEG measures the electric signals of the brain via electrodes placed on various places on the scalp. These signals can be used to study complex neuropsychiatric issues. Studies have shown that EEG has the potential to be used as a biomarker for various neurological conditions including ASD. This chapter will outline the usage of EEG measurement for the classification of ASD using machine learning algorithms.

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