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Exploring deep residual network based features for automatic schizophrenia detection from EEG.

Siuly Siuly1,2, Yanhui Guo3, Omer Faruk Alcin4

  • 1Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Australia. siuly.siuly@vu.edu.au.

Physical and Engineering Sciences in Medicine
|March 22, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a deep residual network to automatically extract features from electroencephalogram (EEG) data for schizophrenia diagnosis. The novel approach achieved 99.23% accuracy using a support vector machine classifier, outperforming existing methods.

Keywords:
ClassificationDeep residual networkElectroencephalogram (EEG) signalFeature extractionSchizophrenia detection

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Area of Science:

  • Neuroscience
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Schizophrenia is a severe mental illness leading to lifelong disability.
  • Current Electroencephalogram (EEG)-based schizophrenia diagnosis relies on manual, time-consuming feature extraction.
  • Existing methods often struggle to balance accuracy and efficiency in feature extraction.

Purpose of the Study:

  • To develop an automated feature extraction method for EEG-based schizophrenia diagnosis.
  • To introduce a deep residual network (ResNet) for identifying hidden patterns in EEG signals.
  • To improve the accuracy and efficiency of schizophrenia detection from EEG data.

Main Methods:

  • Signal pre-processing using an average filtering method.
  • Feature extraction via a deep ResNet architecture.
  • Classification using a softmax layer and comparison with machine learning techniques (SVM).

Main Results:

  • The deep ResNet model automatically extracted representative features from EEG data.
  • The deep features combined with a Support Vector Machine (SVM) classifier achieved 99.23% accuracy.
  • The proposed method demonstrated superior performance compared to existing approaches and the ResNet classifier alone.

Conclusions:

  • The proposed deep ResNet strategy effectively identifies biomarkers for automatic schizophrenia diagnosis from EEG.
  • This approach facilitates the development of computer-assisted diagnostic systems for specialists.
  • Automated feature extraction offers a more accurate and efficient alternative to traditional methods.