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A Computerized Method for Automatic Detection of Schizophrenia Using EEG Signals.

Siuly Siuly, Smith K Khare, Varun Bajaj

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |September 8, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study developed an automatic schizophrenia (SZ) identification method using electroencephalogram (EEG) signals. The approach achieved high accuracy, offering a potential tool to aid psychiatrists in diagnosing SZ.

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    Area of Science:

    • Neurology
    • Biomedical Engineering
    • Data Science

    Background:

    • Schizophrenia diagnosis relies on subjective psychiatric interviews, which are time-consuming and prone to bias.
    • Objective diagnostic tools are needed to support clinical decision-making and research in schizophrenia.

    Purpose of the Study:

    • To develop an automated schizophrenia identification system using electroencephalogram (EEG) signals.
    • To overcome the limitations of traditional diagnostic methods for schizophrenia.

    Main Methods:

    • Utilized Empirical Mode Decomposition (EMD) to analyze non-stationary and nonlinear EEG signals.
    • Extracted 22 statistical features from intrinsic mode functions (IMFs) and selected the top 5 using Kruskal Wallis test.
    • Evaluated classifier performance on a schizophrenia EEG dataset, with ensemble bagged tree as the best model.

    Main Results:

    • The ensemble bagged tree classifier achieved a 93.21% correct classification rate for schizophrenia.
    • An overall accuracy of 89.59% was obtained for the second intrinsic mode function (IMF 2).
    • EEG signals effectively differentiate schizophrenia patients from healthy controls.

    Conclusions:

    • EEG signals show significant potential as an objective tool for supporting schizophrenia diagnosis.
    • The developed automated system can assist clinicians and researchers in identifying schizophrenia.
    • This method offers a promising, data-driven approach to complement psychiatric evaluations.