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Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors.

Sarah Alhassan1,2, Adel Soudani1, Manan Almusallam2

  • 1Department of Computer Science, College of Computer and Information Science, King Saud University, Riyadh 11362, Saudi Arabia.

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Summary

This study introduces an energy-efficient method for early autism detection using electroencephalography (EEG) signals. The new approach significantly reduces data transmission, enhancing wearable sensor lifespan for e-health applications.

Keywords:
Autism Spectrum Disorder detectionEEG signalembedded machine learningon-node feature extraction and classificationwearable sensors

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Computer Science

Background:

  • Wearable wireless systems offer e-health solutions for neurological disorder diagnosis.
  • Electroencephalography (EEG) is crucial for identifying neural abnormalities in autism spectrum disorders (ASD).
  • Transmitting raw EEG data strains wireless sensor lifespan and impacts e-health feasibility.

Purpose of the Study:

  • To develop a sensor-based scheme for early-age autism detection.
  • To implement an energy-efficient signal transformation method for relevant feature extraction.
  • To improve the feasibility of wearable EEG systems for autism diagnosis.

Main Methods:

  • Developed a sensor-based scheme for early autism detection.
  • Implemented an energy-efficient signal transformation technique.
  • Utilized machine learning algorithms for accurate classification of EEG features.

Main Results:

  • Achieved 96% accuracy, 100% sensitivity, and 95% F1 score using machine learning models.
  • Demonstrated a 97% reduction in energy consumption compared to raw EEG streaming.
  • Validated the effectiveness of the proposed energy-efficient scheme.

Conclusions:

  • The proposed scheme enables accurate and energy-efficient early autism detection.
  • Reduced data transmission conserves sensor energy, extending wireless network lifespan.
  • This approach enhances the practicality of wearable EEG for e-health diagnostics.