Skip to main content

Advertisement

SpringerLink
Log in

Neural Network-Based Diagnosing for Optic Nerve Disease from Visual-Evoked Potential

  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

In this paper, we purpose a diagnostic procedure to identify the optic nerve disease from visual evoked potential (VEP) signals using an Artificial Neural Network (ANN). Multilayer feed forward ANN trained with a Levenberg Marquart backpropagation algorithm was implemented. The correct classification rate was 96.87% for subjects having optic nerve disease and 96.66% for healthy subjects. The end results are classified as healthy and diseased. Testing results were found to be compliant with the expected results that are derived from the physician’s direct diagnosis, angiography, VEP and pattern electroretinography. The stated results show that the proposed method could point out the ability of design of a new intelligent assistance diagnosis system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Heckenlively, J. R., and Arden, G. B., Principles and Practice of Clinical Electrophysiology of Vision. Mosby Year Book: United States of America, 1991.

    Google Scholar 

  2. Tasman, W., Duane's Foundations of Clinical Ophthalmology. Lippincott Williams and Wilkins: United States of America, 1992.

    Google Scholar 

  3. Vernon Odom, J., at all, Visual evoked potentials standard, Documenta Ophthalmologica, 2004 Kluwer Academic Publishers. Printed in the Netherlands, 108:115–123, 2004.

  4. Maffei, L., and Fiorentini, A., Electroretinographic responses to alternating gratings before and after sectioning of the optic nerve. Science. 211:953, 1981.

    Article  Google Scholar 

  5. Sivakumar, R., and Ravindran, G., Automatic discrimination of abnormal subjects using the visual evoked potential spectral components. J. Biomed. Biotechnol. 1:5–9, 2004.

    Article  Google Scholar 

  6. Sivakumar, R., Ravindran, G., Muthayya, M., Lakshminarayanan, S. and Velmurughendran, C. U., Diabetic retinopathy analysis. J. Biomed. Biotechnol. 1:20–27, 2005.

    Article  Google Scholar 

  7. Rutecka, A., et al. The study of PSVEP with dysthyroid optic neuropathy. In: 36th Symposium International Society for Clinical Electrophysiology of Vision. Hradce Kralove, Czech Republic; 1998: Abstracts.

  8. Lisboa, P. J. G., Ifeachor, E. C., and Szczepaniak, P. S., Artificial Neural Networks in Biomedicine. Springer-Verlag: London, 2000.

    Google Scholar 

  9. Salvi, M., Dazzi, D., Pelistri, I., Neri, F., and Wall, J. R., Classification and prediction of the progression of thyroid-associated ophthalmopathy by an artificial neural network. Ophthalmology. 109(9):1703–1708, 2002.

    Article  Google Scholar 

  10. Vuckovic, A., Radivojevic, V., Chen, A. C. N., and Popovic, D., Automatic recognition of alertness and drowsiness from EEG by an artificial neural network. Med. Eng. Phys. 24:349–360, 2002.

    Article  Google Scholar 

  11. Tigges, P., Kathmann, N., and Engel, R. R., Identification of input variables for feature based artificial neural networks-saccade detection in EOG recordings. Int. J. Med. Inform. 45:175–184, 1997.

    Article  Google Scholar 

  12. Güven, A., and Kara, S., Classification of electro-oculogram signals using artificial neural network. Expert Syst. Appl. 31(1):199–205, 2006.

    Article  Google Scholar 

  13. Lipoth, L. L., Hafez, H. M. and Goubran, R. A., Elecroretinographical (ERG) Based Classification of Eye Diseases. Ann. Int. Conf. IEEE Eng. Med. Biol. Soc. 13(3):1991.

  14. Kara, S., Güven, A., and Öztürk Öner, A., Utilization of Artificial Neural Networks in the Diagnosis of Optic Nerve Diseases. Comput. Biol. Med. 36(4):428–437, 2006.

    Article  Google Scholar 

  15. Kara, S., and Güven, A., Training a Learning Vector Quantization Network Using the Pattern Electroretinography Signals. Comput. Biol. Med. 37(1):77–82, 2007.

    Article  Google Scholar 

  16. Güven, A., and Kara, S., Diagnosis of the macular diseases from pattern electroretinography signals using artificial neural networks. Expert Syst. Appl. 30(2):361–366, 2006.

    Article  Google Scholar 

  17. Kara, S., Güven, A., Okandan, M., and Dirgenali, F., Utilization of artificial neural networks and autoregressive modeling in diagnosing mitral valve stenosis. Comput. Biol. Med. 36(5):473–483, 2006.

    Article  Google Scholar 

  18. Nguyen, H. T., Butler, M., Roychoudhry, A., Shannon, A. G., Flack, J., and Mitchell, P., Classification of diabetic retinopathy using neural networks, 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Amsterdam, 1548–1549, 1996.

  19. Wright, I. A., and Gough, N. A. J., Artificial neural network analysis of common femoral artery Doppler shift signals: Classification of proximal disease. Ultrasound Med. Biol. 24(5):735–743, 1999.

    Article  Google Scholar 

  20. Beale, R., and Jackson, T., Neural computing: an introduction. Bristol, UK: Institute of Physics Publishing, 1990.

    MATH  Google Scholar 

  21. Haykin, S., Neural networks: a comprehensive foundation. New York: Macmillan College Publishing Company Inc., New York, 1994.

    MATH  Google Scholar 

  22. Simpson, P. K., Artificial Neural Systems. Pergamon Press, 1989.

  23. Long, W. J., Medical informatics: Reasoning methods. Artif. Intel. Med. 23:71–87, 2001.

    Article  Google Scholar 

  24. Lynn, P. A., An introduction to the analysis and processing of signals. (2nd ed) London, UK: Macmillan, 1982.

    Google Scholar 

Download references

Acknowledgement

This project was supported as Post-Graduate Education and Research Project by Erciyes University (Project no: FBT-04-27).

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Eng., Erciyes University, 38039, Kayseri, Turkey

    Sadık Kara

  2. Civil Aviation School, Department of Electronics, Erciyes University, 38039, Kayseri, Turkey

    Ayşegül Güven

Authors
  1. Sadık Kara
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ayşegül Güven
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sadık Kara.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kara, S., Güven, A. Neural Network-Based Diagnosing for Optic Nerve Disease from Visual-Evoked Potential. J Med Syst 31, 391–396 (2007). https://doi.org/10.1007/s10916-007-9081-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-007-9081-0

Keywords

Search

Navigation