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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 27, 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

Gabor filter-based statistical features for ADHD detection.

E Sathiya¹, T D Rao¹, T Sunil Kumar²

¹Division of Mathematics, Vellore Institute of Technology, Chennai, India.

Frontiers in Human Neuroscience

|April 25, 2024

Summary

This summary is machine-generated.

This study introduces a computer-aided method using electroencephalogram (EEG) signals to detect attention deficit/hyperactivity disorder (ADHD) in children. The novel approach achieved 96.4% accuracy, outperforming existing techniques for ADHD diagnosis.

Keywords:

ADHD EEG classification Gabor filter attention deficit/hyperactivity disorder morphological

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

Published on: April 1, 2018

Area of Science:

Neuroscience
Medical Informatics
Signal Processing

Background:

Attention deficit/hyperactivity disorder (ADHD) is a common childhood neuropsychological disorder.
Accurate and early ADHD diagnosis is crucial for effective treatment and intervention.
Current diagnostic methods can be subjective and time-consuming.

Purpose of the Study:

To develop and evaluate a computer-aided diagnostic approach for ADHD detection.
To utilize electroencephalogram (EEG) signals for objective ADHD assessment.
To explore the efficacy of Gabor filter-based statistical features for ADHD classification.

Main Methods:

EEG signals were processed using a bank of Gabor filters to extract narrow-band signals.
Statistical features were extracted from the filtered EEG signals.
Feature selection was performed, and the selected features were used for classification.
A classifier was employed to distinguish between ADHD and healthy control (HC) groups.

Main Results:

The proposed Gabor filter-based statistical features approach achieved a highest classification accuracy of 96.4%.
The method demonstrated superior classification performance compared to existing techniques on a public dataset.
The findings indicate the potential of this approach for objective ADHD detection.

Conclusions:

The developed computer-aided approach shows high accuracy in detecting ADHD using EEG signals.
Gabor filter-based feature extraction offers a promising method for ADHD diagnosis.
This technique could enhance the objectivity and efficiency of ADHD assessment in clinical settings.