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Evaluating the Severity of Autism Spectrum Disorder from EEG: A Multidisciplinary Approach Using Statistical and

Noor Kamal Al-Qazzaz1, Sawal Hamid Bin Mohd Ali2,3, Siti Anom Ahmad4,5

  • 1Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 47146, Iraq.

Bioengineering (Basel, Switzerland)
|November 27, 2025
PubMed
Summary

Electroencephalography (EEG) reveals distinct brain activity patterns in autism spectrum disorder (ASD) that correlate with severity. Machine learning models show promise for early ASD detection using these neurophysiological markers.

Keywords:
ANOVAAutismLSTMMachine learningPearson’s correlationStatistical analysisWaveletdeep learning

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Area of Science:

  • Neuroscience
  • Developmental Psychology
  • Biomedical Engineering

Background:

  • Autism spectrum disorder (ASD) is a developmental impairment affecting social communication and behavior.
  • Understanding the neurophysiological underpinnings of ASD severity is crucial for early intervention.
  • Electroencephalography (EEG) offers a non-invasive method to study brain electrical activity.

Purpose of the Study:

  • To investigate the relationship between EEG spectral-spatial profiles and ASD severity.
  • To apply statistical and artificial intelligence (AI) methods for discriminating ASD severity levels.
  • To explore the potential of EEG-based biomarkers for early ASD diagnosis.

Main Methods:

  • Acquired EEG data from typically developing children and individuals with mild, moderate, and severe ASD.
  • Utilized two-way ANOVA and Pearson's correlation for statistical analysis of relative EEG powers.
  • Employed machine learning classifiers, including Decision Tree (DT) and Long Short-Term Memory (LSTM) neural networks, for classification tasks.

Main Results:

  • A significant correlation was found between ASD severity and neurophysiological activity: faster frequencies decreased, while slower frequencies increased with severity.
  • The Decision Tree (DT) classifier achieved 65% accuracy in discriminating ASD severity levels.
  • The Long Short-Term Memory (LSTM) neural network achieved 73.3% accuracy, demonstrating higher performance.

Conclusions:

  • EEG-based spectro-spatial profiles reveal distinct neurophysiological alterations associated with ASD severity.
  • Statistical and AI techniques show potential for reliable identification of EEG abnormalities in ASD.
  • This approach could facilitate earlier diagnosis and improve treatment outcomes for individuals with ASD.