Employment and comparison of different artificial neural networks for epilepsy diagnosis from EEG signals

Journal of Medical Systems

Volumn 36, Issue 1, 2012, Pages 347-362

  Sezer, Esma ^a   Işik, Hakan ^a   Saracoğlu, Esra ^a  

^a SELCUK UNIVERSITY   (Turkey)

Author keywords

Artificial Neural Networks (ANN); Electroencephalography (EEG); Epilepsy; Principal Component Analysis (PCA); Roc analysis; Wavelet transform

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; ELECTROENCEPHALOGRAPHY; EPILEPSY; PRINCIPAL COMPONENT ANALYSIS; RECEIVER OPERATING CHARACTERISTIC; WAVELET ANALYSIS; HUMAN; METHODOLOGY; SIGNAL PROCESSING;

ELECTROENCEPHALOGRAPHY; EPILEPSY; HUMANS; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS; ROC CURVE; SIGNAL PROCESSING, COMPUTER-ASSISTED; WAVELET ANALYSIS;

EID: 84860227271     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-010-9480-5     Document Type: Article

References (34)

1. Motamedi, G., and Meador, K., Epilepsy and cognition. Department of Neurology, Georgetown University School of Medicine, Washington, 2003.
2. Faught, E., "Epilepsy case studies", USA. Neurol. Clin. 24:291-307, 2006.
3. Blume, T. W., Diagnosis and management of epilepsy. CMAJ 168 (4):441-448, 2003.
4. Beach, R., and Reading, R., The importance of acknowledging clinical uncertainty in the diagnosis of epilepsy and non-epileptic events. Arch. Dis. Child. 90:1219-1222, 2005. (Pubitemid 41736303)
5. Noachtar, S., and Rémi, J., The role of EEG in epilepsy: A critical review. Epilepsy Behav. 15:22-33, 2009.
6. Tzallas, A. T., Tsipouras, M. G., and Fotiadis, D. I, Automatic seizure detection based on time-frequency analysis and ARTIfiCIAL neural networks. Hindawi Publishing Corporation Computational Intelligence and Neuroscience Vol. 2007, Article ID 80510, 13 pages.
7. Patnaika, L. M., and Manyam, O. K., Epileptic EEG detection using neural Networks and post-classification. Comput. Methods Programs Biomed. 91:100-109, 2008.
8. Subasi, A., Epileptic seizure detection using dynamic wavelet network. Expert Systems Appl. 29:343-355, 2005. (Pubitemid 40982998)
9. Ebrahimi, F., Mikaeili, M., Estrada, E., Nazeran, H., and Member, S., Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients. 30th Annual International IEEE EMBS Conference Vancouver, British Columbia, Canada, August 20-24, 2008.
10. Subasi, A., EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems Appl. 32:1084-1093, 2007. (Pubitemid 44821797)
11. Kannathal, N., Puthusserypady, S. K., and Min, L. C., Elman neural networks for dynamic modeling of epileptic EEG. Proceedings of the 28th IEEE EMBS Annual International Conference New York City, USA, Aug 30-Sept 3, 2006.
12. Kiymik, K., İnan, G., Dizibüyük, A., and Akin, M., Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real time application. Comput. Biol. Med. 35(7):603-616, 2004. (Pubitemid 40487489)
13. Subasi, A., and Erçelebi, E., Classification of EEG signals using neural network and logistic regression. Comput. Methods Programs Biomed. 78(2):87-99, 2005. (Pubitemid 40575687)
14. Mohseni, H. R., Maghsoudi, A., and Shamsollahi, B., Seizure detection in EEG signals: A comparison of different approaches. Biomedical Signal and Image Processing Laboratory (BiSIPL), Sharif University of Technology, Tehran, 2006.
15. Jahankhani, P., Kodogiannis, V., and Revett, C., EEG signal classification using wavelet feature extraction and neural networks. IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA'06) pp. 120-124, 2006.
16. Subasi, A., Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients. Expert Syst. Appl. 28:701-711, 2005. (Pubitemid 40583848)
17. Güler, İ., Übeyli, E. D., and Güler, N. F., A mixture of experts network structure for EEG signals classification. Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005.
18. Anderson, C. W., Devulapalli, S. V., and Stolz, E. A., Determining mental state from EEG signals using neural networks. Sci. Program. 4:171-183, 1995.
19. Agarwal, R., Gotman, J., Flanagan, D., and Rosenblatt, B., Automatic EEG analysis during long-term monitoring in the ICU. Electroencephalogr. Clin. Neurophysiol. 107(1):44-58, 1998. (Pubitemid 28343462)
20. Kordylewski, H., Graupe, D., and Liu, K., A novel large-memory neural network as an aid in medical diagnosis applications. IEEE Trans. Inf. Technol. Biomed. 5(3):202-209, 2001. (Pubitemid 32991507)
21. Miner, N. E., An introduction to wavelet theory and analysis. Sandia Report, NM, October 1-25, 1998.
22. Polikar, R., The wavelet tutorial http://www.public.iastate.edu/ %7erpolikar/WAVELETS/WTpart1.html, 2000.
23. Tansel, I. N., Mekdeci, C., Rodriguez, O., and Uragun, B., Monitoring drill conditions with wavelet based encoding and neural networks. Int. J. Mach. Tools Manufact. 33(4):559-575, 1993. (Pubitemid 23673147)
24. Tangirala, A. K., Multirate control and multiscale monitoring of chemical processes. University of Alberta, Edmonton, pp. 116-146, 2001.
25. Naz, E., Farooq, U., and Naz, T., Analysis of principal component analysis-based and fisher discriminant analysis-based face recognition algorithms. 2nd International Conference on Emerging Technologies Peshawar, Pakistan, 13-14 November 2006.
26. García-Altés, A., Santín, D., and Barenys, M., Applying artificial neural networks to the diagnosis of organic dyspepsia. Stat. Methods Med. Res. 16:331-346, 2007. (Pubitemid 47215257)
27. Bhattacharjee, C., Sen, D., Sarkar, P., Data, S., and Bhattacharya, P. K., Studies on the application of different ANNs to predict permeate flux in rotating disk membrane modules: A case study with MATLAB™. Desalin. Water Treat. 2:170-184, 2009.
28. Erkmen, B., and Yildirim, T., Improving classification performance of sonar targets by applying general regression neural network with PCA. Expert Syst. Appl. 35:472-475, 2008.
29. Araghi, L. F., Khaloozade, H., Arva, M. R., Ship identification using probabilistic neural networks (PNN). Proceedings of the International Multi Conference of Engineers and Computer Scientists 2009 Vol. II IMECS 2009, March 18-20, 2009, Hong Kong.
30. Larsson, E., and Fornberg, B., A numerical study of some radial basis function based solution methods for elliptic PDEs. Comput. Math. Appl. 46(5-6):891-902, 2003.
31. Fawcett, T., An introduction to ROC analysis. Pattern Recognit. Lett. 27:861-874, 2006.
32. Metz, C. E., Basic principles of ROC analysis. Sem. Nuc. Med. 8:283-298, 1978. (Pubitemid 9075026)
33. Andrzejak, R. G., Lehnertz, K., Rieke, C., Mormann, F., David, P., and Elger, C. E., Klinik für Epileptologie. Bonn University, Germany, 2001. http://www.epileptologie-Bonn.de/front-content.php?idcat=193&lang= 3&changelang=3.
34. Sezer, E., Epilepsi Teşhisi için EEG Sinyal Analizi. Yüksek Lisans Tezi, Selcuk University, Konya, 2008.