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A self-learned decomposition and classification model for schizophrenia diagnosis.

Smith K Khare1, Varun Bajaj1

  • 1Electronics and Communication Discipline, Indian Institute of Information Technology Design and Manufacturing, Jabalpur, MP, 482005, India.

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Summary
This summary is machine-generated.

This study introduces an advanced computer-aided detection system for schizophrenia (SZ) using electroencephalogram (EEG) signals. The novel method achieves high accuracy in distinguishing SZ patients from healthy controls, offering a more efficient diagnostic tool.

Keywords:
Channel selectionElectroencephalogram signalsFlexible least square support vector machine classifierFlexible tunable Q wavelet transformSchizophrenia detection

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Biomedical Engineering

Background:

  • Schizophrenia (SZ) diagnosis relies on time-consuming manual screening, prone to human error.
  • Electroencephalogram (EEG) signals offer a potential source for SZ detection but are challenging due to non-stationarity.
  • Existing signal decomposition methods can lead to information loss and reduced system performance.

Purpose of the Study:

  • To develop an effective and precise computer-aided detection system for schizophrenia using EEG signals.
  • To propose automatic signal decomposition and classification methods for improved SZ detection.
  • To enhance the accuracy and robustness of EEG-based schizophrenia diagnosis.

Main Methods:

  • Utilized Fisher score for discriminant channel selection.
  • Developed Flexible Tunable Q Wavelet Transform (F-TQWT) with Grey Wolf Optimization (GWO) for signal decomposition, minimizing root mean square error.
  • Extracted and selected features using Kruskal Wallis test, inputting them into a Flexible Least Square Support Vector Machine (F-LSSVM) classifier optimized by GWO.

Main Results:

  • Achieved high performance metrics: 91.39% accuracy, 92.65% sensitivity, 93.22% specificity, 95.57% precision, 0.9306 F-1 measure, 6.78% false positive rate, and 8.61% error.
  • Demonstrated the superiority of the F-TQWT decomposition and F-LSSVM classifier over existing methods.
  • EEG signals from simultaneous motor and auditory tasks showed higher discrimination ability.

Conclusions:

  • The proposed model is accurate, robust, and effective for real-time schizophrenia detection.
  • Simultaneous motor and auditory tasks in EEG recordings enhance diagnostic discrimination.
  • The developed system, validated on a larger dataset with ten-fold cross-validation, is ready for clinical application.