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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Artificial intelligence-based classification of schizophrenia: A high density electroencephalographic and support

Sai Krishna Tikka1, Bikesh Kumar Singh2, S Haque Nizamie3

  • 1Department of Psychiatry, All India Institute of Medical Sciences , Raipur, Chhattisgarh, India.

Indian Journal of Psychiatry
|August 11, 2020
PubMed
Summary

Supervised machine learning using electroencephalography (EEG) effectively differentiates schizophrenia patients from healthy individuals and classifies symptom subgroups. This artificial intelligence approach enhances diagnostic validity and reduces heterogeneity in schizophrenia.

Keywords:
Feature-extractionmachine-learningnegative symptomspositive symptomsvalidity

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Psychiatry

Background:

  • Current interview-based diagnostic methods for schizophrenia (SCZ) lack complete validity.
  • Schizophrenia is a highly heterogeneous disease entity.
  • Artificial intelligence (AI) via supervised machine learning (sML) offers potential solutions.

Purpose of the Study:

  • To evaluate the discriminating validity of resting-state electroencephalographic (EEG) quantitative features using sML.
  • To classify SCZ patients from healthy controls.
  • To classify positive symptom (PS) and negative symptom (NS) subgroups within SCZ.

Main Methods:

  • Utilized a cross-sectional study design with data from 38 SCZ patients and 20 healthy controls.
  • Employed 256-channel high-density EEG recording, selecting eight regions-of-interest.
  • Applied six-level wavelet decomposition and Kernel-Support Vector Machine (SVM) for feature extraction and classification.

Main Results:

  • Achieved 78.95% accuracy in classifying SCZ from healthy controls.
  • Attained 89.29% accuracy in classifying PS from NS SCZ subgroups.
  • Beta and gamma frequencies were key for SCZ vs. healthy classification; delta and theta for PS vs. NS. Inferior frontal gyrus features were most contributory.

Conclusions:

  • SVM-based classification and sub-classification of SCZ using EEG data demonstrate optimal performance.
  • This AI-driven approach may improve diagnostic validity and reduce heterogeneity in SCZ.
  • Findings are generalizable to acute, moderately ill, male SCZ patients.