Classification of attention deficit hyperactivity disorder using machine learning on an EEG dataset.

IF 1.4 4区 心理学 Q4 CLINICAL NEUROLOGY Applied Neuropsychology: Child Pub Date : 2024-01-01 DOI:10.1080/21622965.2023.2300078
Nitin Ahire, R N Awale, Abhay Wagh
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引用次数: 0

Abstract

The neurodevelopmental disorder, Attention Deficit Hyperactivity Disorder (ADHD), frequently affecting youngsters, is characterized by persistent patterns of inattention, hyperactivity, and impulsivity, the etiology of which may involve a variety of genetic, environmental, and neurological factors. Electroencephalography (EEG) measures the electrical activity in the brain through neuronal activity, which is a function of cognitive processes. In this study, a previously recorded sample set of 121 children containing unbiased data from both ADHD and control group classes and EEG signals were analyzed to classify the ADHD patients. The samples were tested under different cognitive conditions, and multiple features were extracted using Euclidean distance. Many machine learning algorithms use Euclidean distance as their default distance metric to compare two recorded data points. The extracted features were trained using four supervised machine learning algorithms (linear regression, random forest, extreme gradient boosting, and K nearest neighbor (KNN)) based on the results of various frequency bands. The results suggest that the KNN algorithm produces the highest accuracy over other machine learning approaches, and results can be further improved with the application of hyperparameter tuning and used for classifying sub-groups of ADHD to identify the severity of the disorder.

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在脑电图数据集上使用机器学习对注意缺陷多动障碍进行分类。
注意力缺陷多动障碍(ADHD)是一种神经发育障碍,经常影响青少年,其特征是注意力不集中、多动和冲动的持续模式,其病因可能涉及遗传、环境和神经系统等多种因素。脑电图(EEG)通过神经元活动测量大脑中的电活动,这是认知过程的一项功能。在这项研究中,先前记录的 121 名儿童样本集包含了多动症和对照组两类儿童的无偏见数据,并对脑电图信号进行了分析,以对多动症患者进行分类。样本在不同的认知条件下进行测试,并使用欧氏距离提取多个特征。许多机器学习算法使用欧氏距离作为默认的距离度量来比较两个记录的数据点。根据不同频段的结果,使用四种有监督的机器学习算法(线性回归、随机森林、极梯度提升和 K 近邻(KNN))对提取的特征进行了训练。结果表明,与其他机器学习方法相比,KNN 算法的准确率最高,而且可以通过调整超参数来进一步提高结果,并可用于对多动症的亚组进行分类,以确定该疾病的严重程度。
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来源期刊
Applied Neuropsychology: Child
Applied Neuropsychology: Child CLINICAL NEUROLOGY-PSYCHOLOGY
CiteScore
4.00
自引率
5.90%
发文量
47
期刊介绍: Applied Neuropsychology: Child publishes clinical neuropsychological articles concerning assessment, brain functioning and neuroimaging, neuropsychological treatment, and rehabilitation in children. Full-length articles and brief communications are included. Case studies of child patients carefully assessing the nature, course, or treatment of clinical neuropsychological dysfunctions in the context of scientific literature, are suitable. Review manuscripts addressing critical issues are encouraged. Preference is given to papers of clinical relevance to others in the field. All submitted manuscripts are subject to initial appraisal by the Editor-in-Chief, and, if found suitable for further considerations are peer reviewed by independent, anonymous expert referees. All peer review is single-blind and submission is online via ScholarOne Manuscripts.
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