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Attention-Deficit/Hyperactivity Disorder

Attention-Deficit/Hyperactivity Disorder

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....

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Related Experiment Video

Updated: Jun 17, 2025

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Published on: March 12, 2020

ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique.

Hui Wen Loh¹, Chui Ping Ooi¹, Shu Lih Oh²

¹School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore.

Cognitive Neurodynamics

|August 6, 2024

Summary

This summary is machine-generated.

A new deep learning system, ADHD/CD-NET, uses EEG data to accurately distinguish between attention deficit hyperactivity disorder (ADHD) and conduct disorder (CD), improving diagnostic accuracy for these complex conditions.

Keywords:

ADHD CNN Conduct disorder Deep learning EEG Explainable artificial intelligence (XAI)Grad-CAM

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Cortical Source Analysis of High-Density EEG Recordings in Children

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Last Updated: Jun 17, 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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Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Area of Science:

Neuroscience
Computational Psychiatry
Medical Imaging

Background:

Attention deficit hyperactivity disorder (ADHD) and conduct disorder (CD) are common childhood neurodevelopmental and behavioral disorders, respectively.
Distinguishing between ADHD and CD is challenging due to overlapping symptoms, increasing the risk of misdiagnosis and inappropriate treatment.
Accurate differentiation is critical as treatment plans differ significantly for ADHD and CD.

Purpose of the Study:

To develop and validate an electroencephalogram (EEG)-based deep learning system, ADHD/CD-NET, for objective differentiation of ADHD, ADHD+CD, and CD.
To assess the diagnostic performance of the proposed system using both internal and external datasets.
To enhance the interpretability of the deep learning model's predictions using Gradient-weighted Class Activation Mapping (Grad-CAM).

Main Methods:

EEG signals were processed into channel-wise continuous wavelet transform (CWT) correlation matrices.
A convolutional neural network (CNN) model (ADHD/CD-NET) was trained using these matrices and evaluated with 10-fold cross-validation.
Grad-CAM was employed to identify significant EEG channels contributing to the diagnostic outcomes.

Main Results:

ADHD/CD-NET achieved high classification accuracy: 93.70% (internal) and 98.19% (external).
The system demonstrated excellent sensitivity (90.83% internal, 98.36% external) and specificity (95.35% internal, 98.03% external).
Grad-CAM successfully highlighted key EEG channels, providing insights into the model's decision-making process.

Conclusions:

ADHD/CD-NET offers a robust and objective method for distinguishing between ADHD, CD, and their comorbidity using EEG data.
The system's ability to perform temporal localization and identify significant channels aids clinical diagnosis.
This deep learning approach provides valuable objective analysis for mental health professionals in diagnosing ADHD and CD.