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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: Dec 9, 2025

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

Published on: April 1, 2018

Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG.

Majid Moghaddari¹, Mina Zolfy Lighvan¹, Sebelan Danishvar²

¹Department of Electronic and Computer Engineering, University of Tabriz, Tabriz, Iran.

Computer Methods and Programs in Biomedicine

|September 14, 2020

Summary

This study developed a deep learning tool using electroencephalography (EEG) to aid in the early diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) in children. The model achieved high accuracy, offering a promising assistive method for physicians.

Keywords:

ADHD Convolutional neural network Deep learning Electroencephalography

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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

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

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Last Updated: Dec 9, 2025

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

Published on: April 1, 2018

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

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
Medical Imaging
Artificial Intelligence

Background:

Attention-Deficit/Hyperactivity Disorder (ADHD) is a common childhood behavioral disorder.
Difficulties in concentration and behavior control are hallmarks of ADHD.
Early diagnosis of ADHD is crucial for effective management but lacks definitive methods.

Purpose of the Study:

To develop an AI-powered assistive tool for physicians.
To identify children with ADHD using electroencephalography (EEG) signals.
To differentiate ADHD patients from healthy controls during a continuous mental task.

Main Methods:

Collected EEG data from 31 children with ADHD and 30 healthy children.
Developed a deep learning model utilizing a convolutional neural network (CNN).
Processed EEG signals, extracted frequency bands (theta, alpha, beta, gamma), converted them into RGB images, and fed them into the CNN for classification.

Main Results:

The CNN model achieved high accuracy rates: 99.06% for segmented samples and 98.48% for subject-based samples.
Performance was further validated using precision, recall, and F1-score metrics via a confusion matrix.
The model demonstrated outstanding performance in classifying ADHD cases.

Conclusions:

The developed deep learning technique significantly outperforms previous methods for ADHD diagnosis in children.
This AI-driven approach shows potential as a reliable assistive tool for early ADHD detection by physicians.
The study highlights the efficacy of EEG-based deep learning for diagnosing pediatric ADHD.