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Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning.

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Title: Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning.
Authors: Ahire, Nitin (AUTHOR), Awale, R.N. (AUTHOR), Wagh, Abhay (AUTHOR)
Source: Applied Neuropsychology: Adult. Jul/Aug2025, Vol. 32 Issue 4, p966-977. 12p.
Subjects: Naive Bayes classification, Attention-deficit hyperactivity disorder, Sensory disorders, Feature selection, Principal components analysis, Oppositional defiant disorder in children
Abstract: "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. [ABSTRACT FROM AUTHOR]
Copyright of Applied Neuropsychology: Adult is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning.
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  Data: <searchLink fieldCode="AR" term="%22Ahire%2C+Nitin%22">Ahire, Nitin</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Awale%2C+R%2EN%2E%22">Awale, R.N.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wagh%2C+Abhay%22">Wagh, Abhay</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Applied+Neuropsychology%3A+Adult%22">Applied Neuropsychology: Adult</searchLink>. Jul/Aug2025, Vol. 32 Issue 4, p966-977. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Naive+Bayes+classification%22">Naive Bayes classification</searchLink><br /><searchLink fieldCode="DE" term="%22Attention-deficit+hyperactivity+disorder%22">Attention-deficit hyperactivity disorder</searchLink><br /><searchLink fieldCode="DE" term="%22Sensory+disorders%22">Sensory disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Oppositional+defiant+disorder+in+children%22">Oppositional defiant disorder in children</searchLink>
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  Group: Ab
  Data: "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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Applied Neuropsychology: Adult is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1080/23279095.2023.2247702
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      – Code: eng
        Text: English
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      – SubjectFull: Attention-deficit hyperactivity disorder
        Type: general
      – SubjectFull: Sensory disorders
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      – SubjectFull: Feature selection
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      – SubjectFull: Principal components analysis
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      – SubjectFull: Oppositional defiant disorder in children
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      – TitleFull: Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning.
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            – D: 01
              M: 07
              Text: Jul/Aug2025
              Type: published
              Y: 2025
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