注意缺陷多动障碍
朴素贝叶斯分类器
脑电图
心理学
阿达布思
焦虑
人工智能
品行障碍
听力学
模式识别(心理学)
分类器(UML)
认知心理学
精神科
发展心理学
计算机科学
支持向量机
医学
作者
Nitin Ahire,R. N. Awale,Abhay Wagh
标识
DOI:10.1080/23279095.2023.2247702
摘要
"Attention-Deficit Hyperactivity Disorder (ADHD)" is a neuro-developmental disorder in children under 12 years old. Learning deficits, anxiety, depression, sensory processing disorder, and oppositional defiant disorder are the most frequent comorbidities of ADHD. This research focuses on ADHD in children, considering its common occurrence and frequent coexistence with other mental disorders. The study utilizes the resting-state open-eye "Electroencephalogram" (EEG) signals of 61 children with ADHD and 60 healthy children. Morphological and "Power Spectral Density" (PSD) features associated with ADHD are analysed and "Principal Component Analysis" (PCA) is employed to reduce data dimensionality. Classification algorithms including AdaBoost, "K-Nearest Neighbour" (KNN) classifier, Naive Bayes, and random forest are utilized, with the Bernoulli Naive Bayes classifier achieving the highest accuracy of 96%. This study found some relevant characteristics for classification at the frontal (F), central (C), and parietal (P) electrode placement sites. Finally, this reveals distinct EEG patterns in children with ADHD and the study provides a potential supplementary method for ADHD diagnosis.
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