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Automatic classification of schizophrenia patients using resting-state EEG signals

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Abstract

Schizophrenia is one of the serious mental disorders, which can suspend the patient from all aspects of life. In this paper we introduced a new method based on the adaptive neuro fuzzy inference system (ANFIS) to classify recorded electroencephalogram (EEG) signals from 14 schizophrenia patients and 14 age-matched control participants. Sixteen EEG channels from 19 main channels that had the most discriminatory information were selected. Possible artifacts of these channels were eliminated with the second-order Butterworth filter. Four features, Shannon entropy, spectral entropy, approximate entropy, and the absolute value of the highest slope of autoregressive coefficients (AVLSAC) were extracted from each selected EEG channel in 5 frequency sub-bands, Delta, Theta, Alpha, Beta, and Gamma. Forty-six features were introduced among the 640 possible ones, and the results included accuracies of near 100%, 98.89%, and 95.59% for classifiers of ANFIS, support vector machine (SVM), and artificial neural network (ANN), respectively. Also, our results show that channels of alpha of O1, theta and delta of Fz and F8, and gamma of Fp1 have the most discriminatory information between the two groups. The performance of our proposed model was also compared with the recently published approaches. This study led to presenting a new decision support system (DSS) that can receive a person’s EEG signal and separates the schizophrenia patient and healthy subjects with high accuracy.

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Acknowledgements

The authors would like to thank the Medical Bioengineering Department, School of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran.

Funding

This work is partially supported by vice-chancellery for research and technology of Tabriz University of Medical Sciences under Grant No. 62321 and ethical code number IR.TBZMED.REC.1398.545. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Medical Bioengineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Golgasht Ave, 51666, Tabriz, Iran

    Hossein Najafzadeh, Mahdad Esmaeili & Seyed Hossein Rasta

  2. Department of Psychiatry, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran

    Sara Farhang

  3. Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Daneshgah St., Tabriz, Iran

    Yashar Sarbaz

  4. Department of Medical Physics, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran

    Seyed Hossein Rasta

  5. Department of Biomedical Physics, School of Medical Sciences, University of Aberdeen, Aberdeen, AB25 ZD, UK

    Seyed Hossein Rasta

Authors
  1. Hossein Najafzadeh
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  2. Mahdad Esmaeili
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  3. Sara Farhang
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  4. Yashar Sarbaz
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  5. Seyed Hossein Rasta
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Contributions

Dr. SHR and Dr. YS conceived of the presented idea and designed the study. Dr.SF supervised the findings of this study. Dr. ME and HN carried out the experiments. All authors discussed the results and contributed to the final manuscript.

Corresponding authors

Correspondence to Yashar Sarbaz or Seyed Hossein Rasta.

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Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

This study was performed in accordance with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Tabriz University of Medical Sciences, Tabriz, Iran (August 27, 2019; REC.1398.545).

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Najafzadeh, H., Esmaeili, M., Farhang, S. et al. Automatic classification of schizophrenia patients using resting-state EEG signals. Phys Eng Sci Med 44, 855–870 (2021). https://doi.org/10.1007/s13246-021-01038-7

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  • DOI: https://doi.org/10.1007/s13246-021-01038-7

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