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Related Concept Videos

Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

59
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by persistent inattention, hyperactivity, and impulsivity. It affects approximately 5-8% of children globally, with around 60-70% of cases persisting into adulthood. ADHD has significant implications for educational attainment, social interactions, and occupational success.
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings....
59

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Related Experiment Video

Updated: Jun 29, 2025

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD
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ADHD-AID: Aiding Tool for Detecting Children's Attention Deficit Hyperactivity Disorder via EEG-Based

Omneya Attallah1,2

  • 1Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 21937, Egypt.

Biomimetics (Basel, Switzerland)
|March 27, 2024
PubMed
Summary

This study introduces ADHD-AID, an automated machine learning tool for identifying attention deficit hyperactivity disorder (ADHD) in adolescents. ADHD-AID significantly improves diagnostic accuracy and efficiency, aiding in early intervention.

Keywords:
attention deficit hyperactivity disorder (ADHD)discrete wavelet transformelectroencephalogram (EEG)empirical wavelet decompositionmachine learningvariational mode decomposition

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Biomedical Engineering

Background:

  • Attention deficit hyperactivity disorder (ADHD) in adolescents has severe consequences, necessitating early identification and intervention.
  • Traditional ADHD diagnostic methods are subjective, time-consuming, and prone to limitations.
  • Existing machine learning (ML) models for ADHD detection often use limited features and do not optimize electrode placement or feature selection.

Purpose of the Study:

  • To develop and evaluate an automated ML-based tool, ADHD-AID, for accurate and efficient ADHD identification in adolescents.
  • To investigate optimal EEG electrode placements and feature selection methods for enhancing ADHD detection accuracy.
  • To overcome the limitations of traditional diagnostic techniques and existing ML models.

Main Methods:

  • Utilized multi-resolution analysis techniques: variational mode decomposition, discrete wavelet transform, and empirical wavelet decomposition.
  • Extracted thirty diverse features (nonlinear, band-power, entropy-based, statistical) from time and time-frequency domains.
  • Employed feature selection methods and analyzed EEG electrode placement for optimal ADHD identification.

Main Results:

  • ADHD-AID achieved high performance metrics: 0.991 accuracy, 0.989 sensitivity, 0.992 specificity, 0.989 F1-score, and 0.982 Matthews correlation coefficient.
  • Demonstrated superior performance compared to previous studies in adolescent ADHD detection.
  • Achieved an Area Under the Curve (AUC) of 0.9958 with 10-fold cross-validation.

Conclusions:

  • ADHD-AID offers a highly accurate and efficient automated solution for adolescent ADHD identification.
  • The tool's performance supports its use as a valuable assistant for clinicians in early ADHD diagnosis.
  • Optimized feature extraction, electrode placement, and feature selection significantly enhance diagnostic capabilities.