An adaptive structure neural networks with application to EEG automatic seizure detection

Neural Networks

Volumn 9, Issue 7, 1996, Pages 1223-1240

  Weng, W ^a   Khorasani, K ^a  

^a Gina Cody School of Engineering and Computer Science   (Canada)

Author keywords

Adaptive structure networks; Back propagation; Neuron generation; Pattern recognition; Training speed

Indexed keywords

ADAPTIVE ALGORITHMS; BACKPROPAGATION; COMPUTER ARCHITECTURE; ELECTROENCEPHALOGRAPHY; LEARNING ALGORITHMS; PATTERN RECOGNITION;

AUTOMATIC EPILEPTIC SEIZURE DETECTION; NEURON GENERATING STRATEGIES; NEURON PRUNING STRATEGIES;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CLINICAL ARTICLE; CONTROLLED STUDY; ELECTROENCEPHALOGRAM; EPILEPSY; HUMAN; PATTERN RECOGNITION; PRIORITY JOURNAL; SEIZURE;

EID: 0030273681     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/0893-6080(96)00032-9     Document Type: Article

References (25)

1. Ash, T. (1989). Dynamic node creation in back propagation networks. Institute for Cognitive Sciences, University of California at San Diego.
2. Azimi-Sadjadi, M. R., & Sheedvash, S. (1991). Recursive node creation in back propagated networks using orthogonal projection method. Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2181-2184, Toronto, Canada.
3. Azimi-Sadjadi M.R., Sheedvash S., Trujillo F. Dynamic node creation in multi-layer neural networks using orthogonal projection method. IEEE Transactions on Neural Networks. 4:1993;242-256.
4. Chen, C. L., & Nutter, R. S. (1991). Improving the training speed of three-layer feedforward neural nets by optimal estimation of the initial weights. Proceedings of IJCNN, pp. 2063-2068, Singapore.
5. Cybenko G. Approximation by superposition of a sigmoidal function. Mathematics of Control, Signals, and Systems. 2:1989;303-314.
6. Davey, B. L. K., Fright, W. R., Carroll, G. J., Jones, R. D. (1989). Expert system approach to detection of epileptiform activity in the EEG. Medical and Biological Engineering and Computing, July, 365-370.
7. Dewan, H. M., & Sontag, E. D. (1990). Extrapolatory method for speeding up the BP algorithm. Proceedings of IJCNN, pp. 613-616, Washington, DC.
8. Fahlman, S. E., & Lebiere, C. (1990). The cascade-correlation learning architecture. Carnegie Mellon University, Internal Report CMU-CS-90-100.
9. Gaobor A.J., Masudseyal K. Automated inertial EEG spike detection using artificial neural networks. Electroencephalography and Clinical Neurophysiology. 83:1992;271-280.
10. Gotman J. Automatic recognition of epileptic seizure in the EEG. Electroencephalography and Clinical Neurophysiology. 54:1982;530-540.
11. Gotman, J. (1985). Seizure recognition and analytis. In J. Gotman, J. R. Ives, & P. Gloor (Eds), Long-term monitoring in epilepsy, electroencephalography and clinical neurophysiology, Suppl. 37. Amsterdam: Elsevier.
12. Gotman J., Wang L. State-dependent spike detection: concepts and preliminary results. Electroencephalography and Clinical Neurophysiology. 79:1991;11-19.
13. Hagiwara, M. (1990). Accelerated back-propagation using unlearning based on Hebb rule. Proceedings of IJCNN, pp. 617-620, Washington, DC.
14. Hassibi, B., Stork, D. G., & Wolff, G. J. (1993). Optimal brain surgeon and general network pruning. IEEE International Conference on Neural Networks (Vol. 1, pp. 293-299). San Francisco, CA.
15. Haykin, S. (1994). Neural networks: A comprehensive foundation. New York: IEEE Press.
16. Hecht-Nielsen, R. (1990). Neurocomputing. Reading, MA: Addison-Wesley.
17. Izui, Y., & Pentland, A. (1990). Speeding up back propagation. Proceedings of IJCNN, pp. 639-642, Washington, DC.
18. Jando G., Seigel R.M., Horvath Z., Buzaki G. Pattern recognition of electroencephalogram by artificial neural networks. Electroencephalography and Clinical Neurophysiology. 86:1993;100-109.
19. Le Cun Y., Denker J.S., Solla S.A. Optimal brain damage. Advances in Neural Information Processing Systems. 2:1990;598-605. San Mateo, CA: Morgan Kaufmann.
20. Lee, T. C. (1991). Structure level adaptation for artificial neural networks. Amsterdam: Kluwer Publishers.
21. Murro A.M., King D.W., Smith J.R., Gallagher B.B., Flanigin H.F., Meador K. Computerized seizure detection of complex partial seizures. Electroencephalography and Clinical Neurophysiology. 79:1991;330-333.
22. Parker, D. B. (1985). Learning logic. Technical Report TR-47, Center for Computational Research in Economics and Management Science. Cambridge, MA: MIT.
23. Qu H., Gotman J. Improvement in seizure detection performance by automatic adaptation to the EEG of each patient. Electroencephalography and Clinical Neurophysiology. 86:1993;79-87.
24. Saarinen, S., Bramley, R. B., & Cybenko, G. (1992). Neural networks, backpropagation, and automatic differentiation. In A. Griewank & G. F. Corliss, (Eds.), Automatic differentiation of algorithms: theory, implementation, and application (pp. 31-42). Philadelphia, PA: SIAM.
25. Waibel A. Consonant recognition of modular construction of large phonemic time-delay neural networks. Advances in Neural Information Processing Systems. 1:1989;. San Mateo, CA: Morgan Kaufmann.