脑电图
自闭症谱系障碍
头皮
自闭症
心理学
听力学
假阳性悖论
认知心理学
发展心理学
人工智能
计算机科学
神经科学
医学
解剖
作者
Sampath Jayarathna,Yasith Jayawardana,Mark Jaime,Sashi Thapaliya
出处
期刊:Advances in bioinformatics and biomedical engineering book series
日期:2019-01-01
卷期号:: 34-65
被引量:16
标识
DOI:10.4018/978-1-5225-7467-5.ch002
摘要
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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