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Related Experiment Video

Updated: Jun 10, 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

Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient

Elham Ahmadi Moghadam¹, Farhad Abedinzadeh Torghabeh¹, Seyyed Abed Hosseini²

¹Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.

Neuroinformatics

|October 18, 2024

Summary

This summary is machine-generated.

This study presents a novel method for diagnosing Attention Deficit Hyperactivity Disorder (ADHD) using fused brain connectivity maps and an attention-based convolutional neural network (Att-CNN). The approach achieved high accuracy, offering a promising tool for early ADHD detection.

Keywords:

ADHD Diagnosis Attention-Based CNN Brain Connectivity EEG Pearson Correlation Coefficient Phase-Locking Value

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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

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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

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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

Area of Science:

Neuroscience
Computational Neuroscience
Medical Imaging

Background:

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurobehavioral disorder in children and adolescents.
Early detection of ADHD is crucial for effective treatment and improved patient outcomes.
Electroencephalogram (EEG) connectivity analysis offers insights into brain network patterns relevant to ADHD diagnosis.

Purpose of the Study:

To develop and evaluate a novel ADHD diagnostic method integrating linear and nonlinear EEG connectivity.
To assess the efficacy of an attention-based convolutional neural network (Att-CNN) for ADHD classification.
To optimize the diagnostic performance by evaluating different optimizers and learning rates.

Main Methods:

EEG data from individuals with and without ADHD were analyzed.
Linear (Pearson Correlation Coefficient - PCC) and nonlinear (Phase-Locking Value - PLV) connectivity measures were computed.
Fused Connectivity Maps (FCMs) were created from various EEG frequency subbands.
An attention-based convolutional neural network (Att-CNN) was employed for classification.
The performance was evaluated using different optimizers (Adam, SGD) and learning rates.

Main Results:

The proposed model achieved high diagnostic performance, with accuracy, precision, recall, and F1 Score reaching 98.88%, 98.41%, 98.19%, and 98.30%, respectively.
Optimal performance was observed using the SGD optimizer with a learning rate of 1e-1 on the theta band FCM.
The integration of FCM and Att-CNN demonstrated significant potential for ADHD diagnosis.

Conclusions:

The developed method combining FCM and Att-CNN shows high accuracy in diagnosing ADHD.
This technique offers a reliable approach for the early detection of ADHD, potentially improving patient outcomes.
The study highlights the utility of advanced machine learning techniques in analyzing complex EEG connectivity data for neurodevelopmental disorders.