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Attention-Deficit/Hyperactivity Disorder01:30

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Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings....
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Attention deficit hyperactivity disorder (ADHD) detection for IoT based EEG signal.

J Aarthy Suganthi Kani1, S Immanuel Alex Pandian2, Anitha J3

  • 1Research Scholar, Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences Karunya Nagar, Coimbatore, Tamil Nadu, India.

Computer Methods in Biomechanics and Biomedical Engineering
|September 20, 2024
PubMed
Summary

This study introduces an innovative IoT-based system for detecting Attention Deficit Hyperactivity Disorder (ADHD) using EEG signals. The proposed method achieves high accuracy, aiding clinicians in objective ADHD diagnosis.

Keywords:
ADHD detectionIDBNPUDMOand fractal dimensionimproved fuzzy feature

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Computer Science

Background:

  • Attention Deficit Hyperactivity Disorder (ADHD) is a common childhood behavioral disorder.
  • Current ADHD diagnosis relies on subjective assessments, lacking objective markers.
  • Early identification is crucial to mitigate ADHD's impact on life outcomes.

Purpose of the Study:

  • To develop an innovative Internet of Things (IoT) based ADHD detection system.
  • To utilize electroencephalogram (EEG) signals for objective ADHD identification.
  • To enhance diagnostic accuracy and support clinical decision-making.

Main Methods:

  • EEG signal processing with min-max normalization.
  • Extraction of advanced features: improved fuzzy features, fractal dimension, wavelet transform, and non-linear features.
  • Development of a hybrid PUDMO algorithm for optimal feature selection and classifier weight tuning.
  • Implementation of a hybrid detection system integrating IDBN and LSTM classifiers.

Main Results:

  • The proposed PUDMO algorithm achieved a high accuracy of 0.9649.
  • Significantly outperformed existing methods (e.g., SLO, SOA, SMA, BRO, DE, POA, DMOA).
  • Demonstrated the effectiveness of the hybrid classifier system in ADHD detection.

Conclusions:

  • The IoT-based EEG analysis offers a promising objective approach for ADHD detection.
  • The hybrid PUDMO algorithm enhances feature selection and classifier performance.
  • This technology can assist clinicians in making more informed and objective ADHD diagnoses.