Neural network-based diagnosing for optic nerve disease from visual-evoked potential

Journal of Medical Systems

Volumn 31, Issue 5, 2007, Pages 391-396

  Kara, Sadik ^a   Güven, Ayşegül ^a  

^a ERCIYES UNIVERSITY   (Turkey)

Author keywords

Artificial neural networks; Levenberg Marquart backpropagation; Optic nerve disease; Visual evoked potential

Indexed keywords

ADULT; ANGIOGRAPHY; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; DIAGNOSTIC PROCEDURE; DISEASE CLASSIFICATION; ELECTRORETINOGRAPHY; EVOKED VISUAL RESPONSE; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MALE; METHODOLOGY; OPTIC NERVE DISEASE; PERFORMANCE MEASUREMENT SYSTEM; PHYSICIAN;

ADULT; ALGORITHMS; EVOKED POTENTIALS, VISUAL; FEMALE; HUMANS; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); OPTIC NERVE DISEASES;

EID: 34548079028     PISSN: 01485598     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10916-007-9081-0     Document Type: Article

References (24)

1. Heckenlively, J. R., and Arden, G. B., Principles and Practice of Clinical Electrophysiology of Vision. Mosby Year Book: United States of America, 1991.
2. Tasman, W., Duane's Foundations of Clinical Ophthalmology. Lippincott Williams and Wilkins: United States of America, 1992.
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.
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.
6. Sivakumar, R., Ravindran, G., Muthayya, M., Lakshminarayanan, S. and Velmurughendran, C. U., Diabetic retinopathy analysis. J. Biomed. Biotechnol. 1:20-27, 2005.
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.
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.
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.
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.
12. Güven, A., and Kara, S., Classification of electro-oculogram signals using artificial neural network. Expert Syst. Appl. 31(1):199-205, 2006.
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.
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.
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.
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.
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.
20. Beale, R., and Jackson, T., Neural computing: an introduction. Bristol, UK: Institute of Physics Publishing, 1990.
21. Haykin, S., Neural networks: a comprehensive foundation. New York: Macmillan College Publishing Company Inc., New York, 1994.
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.
24. Lynn, P. A., An introduction to the analysis and processing of signals. (2nd ed) London, UK: Macmillan, 1982.