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Related Experiment Video

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Automated detection of schizophrenia using nonlinear signal processing methods.

V Jahmunah1, Shu Lih Oh1, V Rajinikanth2

  • 1Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.

Artificial Intelligence in Medicine
|October 15, 2019
PubMed
Summary

This study developed an Automated Diagnostic Tool (ADT) to classify Electroencephalogram (EEG) signals, achieving 92.91% accuracy in distinguishing normal brain activity from schizophrenia. The ADT effectively identifies schizophrenia using EEG patterns.

Keywords:
EEG signalNon-linear feature extractionPerformance evaluation and validationSVM classifierSchizophreniaSeries splitting

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Area of Science:

  • Neuroscience and biomedical engineering.
  • Application of advanced signal processing techniques to neuroimaging data.

Background:

  • Electroencephalogram (EEG) analysis is crucial for understanding brain conditions and detecting abnormalities.
  • Schizophrenia diagnosis can be challenging, necessitating improved objective diagnostic tools.

Purpose of the Study:

  • To develop an Automated Diagnostic Tool (ADT) for classifying EEG signals into normal and schizophrenia categories.
  • To investigate the efficacy of non-linear feature extraction and machine learning for EEG-based schizophrenia detection.

Main Methods:

  • EEG signal processing involved series splitting, non-linear feature mining, and t-test assisted feature selection.
  • A dataset of 19-channel EEG signals from normal and schizophrenia subjects was utilized.
  • Classification was performed using Decision-Tree, Linear-Discriminant analysis, k-Nearest-Neighbour, Probabilistic-Neural-Network, and Support-Vector-Machine (SVM) with Radial-Basis-Function (RBF) kernel.

Main Results:

  • The ADT generated 1142 features from split EEG data, reduced to 157 non-linear features, and further selected 14 principal features.
  • Support-Vector-Machine with Radial-Basis-Function (SVM-RBF) demonstrated the highest classification performance.
  • The SVM-RBF classifier achieved a superior average performance of 92.91% on the EEG dataset.

Conclusions:

  • The developed Automated Diagnostic Tool (ADT) shows significant potential for objective schizophrenia diagnosis using EEG data.
  • SVM-RBF is a highly effective classifier for distinguishing between normal and schizophrenia EEG patterns.
  • This automated approach offers a promising advancement in the neurophysiological assessment of schizophrenia.