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Related Concept Videos

Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

34
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....
34

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Early attention-deficit/hyperactivity disorder (ADHD) with NeuroDCT-ICA and rhinofish optimization (RFO) algorithm

Ahmed Alhussen1, Ahmed Ibrahim Alutaibi1, Sunil Kumar Sharma2

  • 1Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, 11952, Saudi Arabia.

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|February 26, 2025
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Summary

This study introduces an advanced ADHD detection system using deep learning. The novel approach significantly improves accuracy and efficiency in identifying Attention Deficit Hyperactivity Disorder patterns from EEG data.

Keywords:
ADHD detectionADHD-AttentionNetDeep learningHybrid optimizationNeuroDCT-ICARhinoFish optimization

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Existing methods for detecting Attention Deficit Hyperactivity Disorder (ADHD) face limitations in data processing, accuracy, and computational efficiency.
  • Deep Learning (DL) frameworks present a transformative potential for enhancing ADHD detection systems.

Purpose of the Study:

  • To develop a sophisticated ADHD detection framework that overcomes the drawbacks of current approaches.
  • To leverage DL for improved accuracy, data processing, and reduced computational time in ADHD identification.

Main Methods:

  • A novel NeuroDCT-ICA module was developed for preprocessing raw EEG data, focusing on noise elimination and feature extraction.
  • A new RhinoFish Optimization (RFO) algorithm was introduced for optimal feature selection, enhancing system stability and data processing.
  • An ADHD-AttentionNet, a DL-based model, was employed as the core component for ADHD identification.

Main Results:

  • The proposed model achieved high performance metrics, including an accuracy of 98.52%, an F-score of 98.26%, and a specificity of 98.16%.
  • The NeuroDCT-ICA module effectively eliminated noise and extracted informative features from EEG data.
  • The RFO algorithm enhanced system stability and data processing capacity.

Conclusions:

  • The developed DL-based framework offers a more accurate and efficient method for ADHD detection.
  • The integration of NeuroDCT-ICA, RFO, and ADHD-AttentionNet demonstrates superior performance in identifying ADHD-related patterns.
  • This advanced approach holds significant promise for clinical applications in ADHD diagnosis.