{"title":"Classification of attention deficit hyperactivity disorder using machine learning on an EEG dataset.","authors":"Nitin Ahire, R N Awale, Abhay Wagh","doi":"10.1080/21622965.2023.2300078","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":8047,"journal":{"name":"Applied Neuropsychology: Child","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Neuropsychology: Child","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1080/21622965.2023.2300078","RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
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.
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.