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Schizophrenia EEG Signal Classification Based on Swarm Intelligence Computing.

Sunil Kumar Prabhakar1, Harikumar Rajaguru2, Sun-Hee Kim1

  • 1Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Republic of Korea.

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Summary
This summary is machine-generated.

This study enhances schizophrenia detection using electroencephalography (EEG) signals. Optimized feature selection with Black Hole (BH) optimization and Support Vector Machine-Radial Basis Function (SVM-RBF) achieved high classification accuracy for both normal and schizophrenia cases.

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Area of Science:

  • Human neuroscience
  • Computational psychiatry
  • Biomedical signal processing

Background:

  • Schizophrenia is a severe mental disorder characterized by abnormal reality interpretation, disordered thinking, delusions, and hallucinations.
  • Electroencephalography (EEG) signals are valuable for analyzing brain activity and diagnosing neurological disorders, including schizophrenia.
  • Effective classification of schizophrenia using EEG requires robust feature extraction and optimization techniques.

Purpose of the Study:

  • To develop an accurate classification method for schizophrenia using EEG signals.
  • To extract and optimize a comprehensive set of features from EEG data.
  • To evaluate the performance of different optimization algorithms and classifiers for schizophrenia detection.

Main Methods:

  • Extraction of diverse features from EEG signals, including DFA, Hurst Exponent, RQA, Sample Entropy, FD, Kolmogorov Complexity, Hjorth exponent, LZC, and LLE.
  • Optimization of extracted features using Artificial Flora (AF), Glowworm Search (GS), Black Hole (BH), and Monkey Search (MS) algorithms.
  • Classification of optimized features using Support Vector Machine-Radial Basis Function (SVM-RBF) kernel.

Main Results:

  • The Black Hole (BH) optimization algorithm combined with the SVM-RBF classifier yielded the highest accuracy.
  • Classification accuracy reached 87.54% for normal cases and 92.17% for schizophrenia cases.
  • Feature optimization significantly improved the discriminative power of EEG signals for schizophrenia detection.

Conclusions:

  • The proposed method demonstrates high efficacy in classifying schizophrenia using EEG signals.
  • Black Hole optimization is a powerful tool for selecting relevant features in EEG-based psychiatric disorder analysis.
  • This approach holds promise for improving the diagnosis and understanding of schizophrenia through neurophysiological data analysis.