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Related Experiment Video

Updated: Oct 23, 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

Detection of ADHD From EEG Signals Using Different Entropy Measures and ANN.

R Catherine Joy¹, S Thomas George², A Albert Rajan³

¹Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India.

Clinical EEG and Neuroscience

|August 23, 2021

Summary

This summary is machine-generated.

This study introduces a computer-aided system using electroencephalography (EEG) signals to detect attention deficit hyperactivity disorder (ADHD). Permutation entropy analysis of EEG data achieved high accuracy in identifying ADHD, offering a more efficient diagnostic approach.

Keywords:

EEG artificial neural network attention deficit hyperactivity disorder classification entropy

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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

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Last Updated: Oct 23, 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

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

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Area of Science:

Neuroscience and Biomedical Engineering
Computational Psychiatry

Background:

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children.
Current ADHD diagnostic methods, including clinical evaluations and symptom surveys, are time-consuming and may lack precision.

Purpose of the Study:

To develop and evaluate an efficient computer-aided system for ADHD detection using electroencephalography (EEG) signals.
To compare the efficacy of various nonlinear entropy estimators for discriminating ADHD subjects from controls.

Main Methods:

Acquisition of EEG signals from ADHD subjects and a control group.
Feature extraction using nonlinear entropy estimators: fuzzy entropy, log energy entropy, permutation entropy, SURE entropy, and Shannon entropy.
Classification of subjects using an artificial neural network (ANN) classifier.

Main Results:

The computer-aided system effectively detected and classified ADHD subjects.
Permutation entropy yielded the highest classification accuracy (99.82%), sensitivity (98.21%), and specificity (98.82%).
Shannon entropy showed limited diagnostic scope compared to other estimators, indicated by a higher P-value (>0.001).
Significant signal variations in frontal polar (FP) and frontal (F) lobes under eyes-closed conditions were identified as crucial for ADHD differentiation.

Conclusions:

Nonlinear entropy analysis of EEG signals, particularly permutation entropy, offers a highly accurate and efficient method for ADHD detection.
EEG signal analysis in frontal brain regions holds significant potential for improving ADHD diagnosis.
This computational approach provides a promising alternative to traditional, time-intensive ADHD assessment methods.