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An Intelligent System for Diagnosing Schizophrenia and Bipolar Disorder based on MLNN and RBF

Authors(5):

M. I. Elgohary, Tamer. A. Alzohairy, Amir. M. Eissa, Sally. Eldeghaidy, Hussein. M
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This paper is concerned with the problem of discriminating the patients suffering from schizophrenia and bipolar disorder versus control based on EEG rhythms. The EEG rhythms are used to extract a feature vector for each patient. In this paper, the large set of features included in the EEG rhythms is reduced into smaller set of features using FFT segmentation. Two classes of classifiers which are multi-layer perceptron and radial basis function are used to discriminate the patient data based on features vector. Experimental studies have shown that the proposed algorithms give excellent results when applied and tested on the three classes. The multilayer neural network with backpropagation achieved a high performance rate equal to 98.67 % compared to radial basis function networks which achieved a performance rate equal to 87.33%.

M. I. Elgohary, Tamer. A. Alzohairy, Amir. M. Eissa, Sally. Eldeghaidy, Hussein. M

EEG, schizophrenia, Bipolar disorder, artificial neural networks, Backpropagation Algorithm, Radial basis function network.

  1. American Psychiatric Association & American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (DSM). Washington, DC: American psychiatric association, 143-7.
  2. V. Kusumakar, “Antidepressants and antipsychotics in the long-term treatment of bipolar disorder,” Journal of Clinical Psychiatry, vol. 63, Suppl 10, pp. 23–28, 2002.
  3. Sachs, G. S., Printz, D. J., Kahn, D. A., Carpenter, D., & Docherty, J. P. (2000). The expert consensus guideline series: medication treatment of bipolar disorder. Postgrad Med, 1, 1-104.
  4. A. J. Niemiec and B. J. Lithgow, “Alpha-band characteristics in EEG spectrum indicate reliability of frontal brain asymmetry measures in diagnosis of depression,” in Proceedings Int. Conf. of the IEEE Eng. in Medicine and Biology Society, pp. 7517–7520, Sept. 2005.
  5. P. Coutin-Churchman, et al., “Quantitative spectral analysis of EEG in psychiatry revisited: drawing signs out of numbers in a clinical setting,” Clinical Neurophysiology, vol. 114, no. 12, pp. 2294–2306, Dec. 2003.
  6. Li, Y. J., & Fan, F. Y. (2006, January). Classification of Schizophrenia and depression by EEG with ANNs. In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference (pp. 2679-2682). IEEE.
  7. M. Liu, “A Study of Schizophrenia Inheritance through Pattern Classification”, 2nd International Conference on Intelligent Control and Information Processing, Changsa, 2011.
  8. Alba-Sanchez F., “Assisted Diagnosis of Attention-Deficit Hyperactivity Disorder through EEG Bandpower Clustering with Self-Organizing Maps”, 32nd Annual International Conference of the IEEE EMBS, Argentina, September 2010.
  9. Hiesh, Ming-Hsien, et al. "Classification of schizophrenia using genetic algorithm-support vector machine (ga-svm)." 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013.
  10. http://www.elazayem.com/elazayem%20hospital.htm
  11. Arnaud Delorme, Scott Makeig. “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis”. Neurosci Methods. Vol. 134. pp.9-21. 2004.
  12. El-Gohary, M. I., Al-Zohairy, T. A., Eissa, A. M., Eldeghaidy, S. M., & El-Hafez, H. M. A. (2015). EEG Discrimination of Rats under Different Architectural Environments using ANNs. International Journal of Computer Science and Information Security, 13(12), 24.
  13. Hastie, T., Tibshirani, R., & Friedman, J. (2009). Unsupervised learning. In The elements of statistical learning (pp. 485-585). Springer New York. ISO 690
  14. Rao, V., and Rao, H., (1996). C+ + Neural Networks and Fuzzy Logic, 1st edn. BPB Publications, New Delhi.

Publication Details

Published in : Volume 2 | Issue 4 | July-August - 2016
Date of Publication Print ISSN Online ISSN
2016-08-30 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
117-123 IJSRSET162430   Technoscience Academy

Cite This Article

M. I. Elgohary, Tamer. A. Alzohairy, Amir. M. Eissa, Sally. Eldeghaidy, Hussein. M, "An Intelligent System for Diagnosing Schizophrenia and Bipolar Disorder based on MLNN and RBF ", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 4, pp.117-123, July-August-2016.
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