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<p>To realize the automatic identification of electroencephalogram (EEG) signals from patients with schizophrenia, this study calculated graph-theoretic features derived from event-related potentials (ERP) recorded during a Go/NoGo task. The ERP data were collected from 42 individuals diagnosed with schizophrenia and 29 healthy controls. Different functional networks of single-frequency band and all band of these EEG data, were constructed by phase locking value method. Their graph-theoretic features were extracted. Three machine learning classifiers were used to automatically distinguish the patients and health controls. The classification accuracies of different models with graph-theoretic features of different networks were compared. The results revealed that F-score combined with support vector machine (SVM) model achieved the highest accuracy. This model with graph-theoretic features of all-band network and theta band networks resulted in the highest classification accuracy, which was 95.45%. It suggested that the SVM with graph-theoretic features can be considered as a potential method for schizophrenia identification.</p>

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Schizophrenia Identification Using Machine Learning Methods with Graph-Theoretic Features

  • 期刊论文

  • 发表日期：

    2025年4月4日

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  • Geng Zhu,
  • Qi Xu,
  • Fengzhu Zhang,
  • Yaqin Yang,
  • Xiaoou Li,
  • Bin Li,
  • Shengtian Li

摘要

To realize the automatic identification of electroencephalogram (EEG) signals from patients with schizophrenia, this study calculated graph-theoretic features derived from event-related potentials (ERP) recorded during a Go/NoGo task. The ERP data were collected from 42 individuals diagnosed with schizophrenia and 29 healthy controls. Different functional networks of single-frequency band and all band of these EEG data, were constructed by phase locking value method. Their graph-theoretic features were extracted. Three machine learning classifiers were used to automatically distinguish the patients and health controls. The classification accuracies of different models with graph-theoretic features of different networks were compared. The results revealed that F-score combined with support vector machine (SVM) model achieved the highest accuracy. This model with graph-theoretic features of all-band network and theta band networks resulted in the highest classification accuracy, which was 95.45%. It suggested that the SVM with graph-theoretic features can be considered as a potential method for schizophrenia identification.