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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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Updated: Aug 20, 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

Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related

Elham Ghasemi¹, Mansour Ebrahimi^2,3, Esmaeil Ebrahimie^2,4,5,6

¹Institute of Biotechnology, Shiraz University, Shiraz, Iran.

Cognitive Neurodynamics

|November 21, 2022

Summary

Machine learning accurately diagnoses Attention-Deficit/Hyperactivity Disorder (ADHD) using brain signals. This expert system improves diagnostic accuracy, minimizing misdiagnosis and aiding treatment evaluation.

Keywords:

Attention deficit hyperactivity disorder Band power Classification Event-related potentials Frequency bands Machine learning

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Event-related Potentials During Target-response Tasks to Study Cognitive Processes of Upper Limb Use in Children with Unilateral Cerebral Palsy

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Published on: January 11, 2016

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Last Updated: Aug 20, 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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Published on: March 12, 2020

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Published on: April 1, 2018

Event-related Potentials During Target-response Tasks to Study Cognitive Processes of Upper Limb Use in Children with Unilateral Cerebral Palsy

Event-related Potentials During Target-response Tasks to Study Cognitive Processes of Upper Limb Use in Children with Unilateral Cerebral Palsy

Published on: January 11, 2016

Area of Science:

Neuroscience
Computational psychiatry
Machine learning

Background:

Accurate diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) remains a significant clinical challenge.
Misdiagnosis of ADHD can lead to adverse medical outcomes.
Current diagnostic methods lack computational expert systems due to the disorder's complexity.

Purpose of the Study:

To develop an accurate machine learning model for discriminating between ADHD patients and healthy individuals.
To explore pattern discovery in brain signals for ADHD diagnosis.
To minimize misdiagnosis rates and aid in treatment efficacy evaluation.

Main Methods:

Collected Event-Related Potentials (ERP) data from ADHD patients and controls.
Pre-processed ERP signals, decomposed them, and calculated features across frequency bands.
Utilized seven machine learning algorithms for classification, feature selection, and combination.

Main Results:

Combined complementary features significantly improved predictive model performance.
Newly developed band power features achieved >99.85% accuracy and >0.999 AUC with specific models (GLM, Logistic Regression, Deep Learning).
High (Beta) and low (Delta) frequencies were more effective than mid-frequencies for ADHD discrimination.

Conclusions:

A machine learning expert system was developed for ADHD diagnosis.
The system demonstrates high accuracy in discriminating ADHD patients from controls.
This approach minimizes misdiagnosis and can assist in evaluating treatment effectiveness.