Artificial Intelligence in Medicine
Volume 43, Issue 2 , Pages 141-149 , June 2008

Ensemble adaptive network-based fuzzy inference system with weighted arithmetical mean and application to diagnosis of optic nerve disease from visual-evoked potential signals

  • Bayram Akdemir

      Affiliations

    • Selcuk University, Department of Electrical & Electronics Engineering, 42075 Konya, Turkey
    • ,
  • Sadık Kara

      Affiliations

    • Fatih University, Department of Electrical and Electronics Engineering, 34500 Istanbul, Turkey
    • Corresponding Author InformationCorresponding author. Tel.: +90 212 90 8663300x5593; fax: +90 212 8663412.
    • ,
  • Kemal Polat

      Affiliations

    • Selcuk University, Department of Electrical & Electronics Engineering, 42075 Konya, Turkey
    • ,
  • Ayşegül Güven

      Affiliations

    • Erciyes University, Department of Biomedical Engineering, 38039, Kayseri, Turkey
    • ,
  • Salih Güneş

      Affiliations

    • Selcuk University, Department of Electrical & Electronics Engineering, 42075 Konya, Turkey

Received 22 March 2007 ,Revised 26 February 2008 ,Accepted 19 March 2008.

References 

  1. Polat K, Şahan S, Kodaz H, Güneş S. Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism. Expert Systems with Applications. 2007;32(1):172–183
  2. Atilla H, Tekeli O, Örnek K, Batioğlu F, Elhan AH, Eryilmaz T. Pattern electroretinography and visual evoked potentials in optic nerve diseases. Journal of Clinical Neuroscience. 2006;13:55–59
  3. Tasman W. Duane's Foundations of Clinical Ophthalmology. United States of America: Lippincott Williams and Wilkins Company; 1992;
  4. Vernon Odom J, Bach M, Barber C, Brigell M, Marmor MF, Tormene AP. Visual evoked potentials standard. Documenta ophthalmologica. vol. 108. Printed in the Netherlands: Kluwer Academic Publishers; 2004;pp. 115–123
  5. Sivakumar R, Ravindran G. Automatic discrimination of abnormal subjects using the visual evoked potential spectral components. Journal of Biomedicine and Biotechnology. 2004;1:5–9
  6. Sivakumar R, Ravindran G, Muthayya M, Lakshminarayanan S, Velmurughendran CU. Diabetic retinopathy analysis. Journal of Biomedicine and Biotechnology. 2005;1:20–27
  7. Rutecka A. The study of PSVEP with dysthyroid optic neuropathy. In: Proceedings of the 36th Symposium International Society for Clinical Electrophysiology of Vision. Hradce Kralove, Czech Republic, June 13–17. 1998;Abstracts
  8. Polat K, Tosun S, Güneş S. Diagnosis of heart disease using artificial immune recognition system and fuzzy weighted pre-processing. Pattern Recognition. 2006;39(11):2186–2193
  9. Wang X, Paliwal KK. Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition. Pattern Recognition, The Journal of The Pattern Recognition Society. 2003;36:2429–2439
  10. Lindsay I Smith, A tutorial on Principal Component Analysis, (http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf) (last accessed, 10 March, 2008).
  11. Jang J-SR. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics. 1993;23(3):665–685
  12. Jang J-SR. Self-learning fuzzy controllers based on temporal backpropagation. IEEE Transactions on Neural Networks. 1992;3(5):714–723
  13. Güler İ, Übeyli ED. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. Journal of Neuroscience Methods. 2005;148(2):13–121
  14. Dirgenali F, Kara S. Recognition of early phase of atherosclerosis using principles component analysis and artificial neural networks from carotid artery Doppler signals. Expert Systems with Applications. 2006;31(3):643–651
  15. Güler I, Ubeyli ED. Detection of ophthalmic artery stenosis by least-mean squares backpropagation neural network. Computers in Biology and Medicine. 2003;33:333–343
  16. Güven A, Polat K, Kara S, Güneş S. The effect of generalized discriminate analysis (GDA) to the classification of optic nerve disease from VEP signals. Computers in Biology and Medicine. 2008;38(1):62–68
  17. Kara S, Güven A, Öner AÖ. Utilization of artificial neural networks in the diagnosis of optic nerve diseases. Computers in Biology and Medicine. 2006;36(4):428–437
  18. Kara S, Güven A, İçer S. Classification of macular and optic nerve disease by principal component analysis. Computers in Biology and Medicine. 2007;37(6):836–841

PII: S0933-3657(08)00036-5

doi: 10.1016/j.artmed.2008.03.007

Artificial Intelligence in Medicine
Volume 43, Issue 2 , Pages 141-149 , June 2008

Article Tools

* Image Usage & Resolution