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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: May 31, 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

EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention

Jayoti Bansal¹, Gaurav Gangwar¹, Mohammad Aljaidi²

¹Department of Computer Science Engineering, Baba Farid College of Engineering & Technology, Bathinda 151001, Punjab, India.

|January 24, 2025

Summary

This summary is machine-generated.

This study introduces a novel deep learning model using electroencephalography (EEG) to objectively diagnose Attention-Deficit/Hyperactivity Disorder (ADHD). The advanced ResNet model achieved high accuracy, offering a promising tool for ADHD diagnosis.

Keywords:

ADHD EEG ResNet auto encoder double augmented attention mechanism reptile search algorithm

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Area of Science:

Neuroscience
Medical Technology
Artificial Intelligence

Background:

Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder with no objective diagnostic tests.
Current ADHD diagnosis relies on subjective psychiatric assessments, necessitating significant clinical effort.
There is a need for objective tools to enhance ADHD diagnostic accuracy and reduce subjectivity.

Purpose of the Study:

To develop an objective diagnostic method for ADHD using electroencephalography (EEG) signal analysis.
To leverage deep learning techniques for improved ADHD diagnosis from EEG data.
To introduce a novel ResNet-based model with a double-augmented attention mechanism for complex EEG pattern recognition.

Main Methods:

Utilized an autoencoder for feature extraction from EEG data.
Employed the Reptile Search Algorithm for optimal feature selection.
Developed and trained a modified ResNet architecture for ADHD classification.

Main Results:

The proposed ResNet model demonstrated superior performance compared to traditional classifiers.
Achieved 99.42% accuracy, 99.03% precision, 99.82% recall, and 99.42% F1-score.
The model achieved a Receiver Operating Characteristic Area Under the Curve (ROC AUC) score of 0.99.

Conclusions:

The ResNet model shows exceptional capability in differentiating between children with and without ADHD.
The objective diagnostic approach minimizes misclassification errors.
This advanced EEG analysis offers improved diagnostic precision for ADHD.