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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Automated identification for autism severity level: EEG analysis using empirical mode decomposition and second order

Hikmat Hadoush1, Maha Alafeef2, Enas Abdulhay3

  • 1Department of Rehabilitation Sciences, Faculty of Applied Medical Sciences at Jordan University of Science and Technology, Jordan.

Behavioural Brain Research
|January 15, 2019
PubMed
Summary
This summary is machine-generated.

This study used electroencephalography (EEG) analysis to differentiate between mild and severe autism spectrum disorder (ASD) in children. The findings show distinct EEG patterns correlating with ASD severity, offering a potential automated diagnostic tool.

Keywords:
Artificial neural networkAutism spectrum disordersCentral tendency measureElectroencephalographyElliptical areaEmpirical mode decompositionSecond-order difference plot

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Developmental Psychology

Background:

  • Automated diagnosis of autism spectrum disorders (ASD) using nonlinear EEG analysis has historically struggled to differentiate severity levels.
  • Previous methods could only distinguish ASD from typical development, not varying degrees of autistic features.

Purpose of the Study:

  • To identify EEG differences between mild and severe ASD using Empirical Mode Decomposition (EMD) and Second-Order Difference Plot (SODP).
  • To determine the accuracy of these EEG analysis models in distinguishing ASD severity.

Main Methods:

  • Resting-state EEG data from 36 children (equally divided into mild and severe ASD groups) were analyzed.
  • EMD was used to extract Intrinsic Mode Functions (IMFs) features, SODP patterns, elliptical area, and Central Tendency Measure (CTM) values.
  • An Artificial Neural Network (ANN) was employed to assess the diagnostic accuracy of the derived EEG measures.

Main Results:

  • Children with severe ASD exhibited less IMF oscillation, more stochastic SODP plotting, lower CTM values, and larger ellipse areas compared to those with mild ASD.
  • These EEG patterns suggest greater variability and impaired behavioral control in severe ASD.
  • The ANN achieved 100% sensitivity, 94.7% specificity, and 97.2% overall accuracy in differentiating between mild and severe ASD groups.

Conclusions:

  • Distinct IMF features, SODP patterns, elliptical area, and CTM values differentiate between mild and severe ASD in children.
  • These EEG-derived measures show promise as a sensitive automated tool for classifying ASD severity levels.