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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 545613, 12 pages
http://dx.doi.org/10.1155/2013/545613
Research Article

Coercively Adjusted Auto Regression Model for Forecasting in Epilepsy EEG

1Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
2Department of Computer Science, Chonnam National University, Gwangju 500-757, Republic of Korea

Received 7 January 2013; Revised 18 March 2013; Accepted 27 March 2013

Academic Editor: Yiwen Wang

Copyright © 2013 Sun-Hee Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. J. L. D. Hoon, T. H. J. J. Van der Hagen, H. Schoonewelle, and H. van Dam, “Why Yule-Walker should not be used for autoregressive modelling,” Annals of Nuclear Energy, vol. 23, no. 15, pp. 1219–1228, 1996. View at Publisher · View at Google Scholar · View at Scopus
  2. S. M. Chen and J. R. Hwang, “Temperature prediction using fuzzy time series,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 30, no. 2, pp. 263–275, 2000. View at Publisher · View at Google Scholar · View at Scopus
  3. P. Kumar and E. Walia, “Cash forecasting: an application of artificial neural networks in finance,” International Journal of Computer Science and Applications, vol. 3, no. 1, pp. 61–77, 2006. View at Google Scholar
  4. R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state,” Physical Review E, vol. 64, no. 6 I, pp. 061907/1–061907/8, 2001. View at Google Scholar · View at Scopus
  5. M. E. Saab and J. Gotman, “A system to detect the onset of epileptic seizures in scalp EEG,” Clinical Neurophysiology, vol. 116, no. 2, pp. 427–442, 2005. View at Publisher · View at Google Scholar · View at Scopus
  6. N. C. Bhavaraju, M. G. Frei, and I. Osorio, “Analog seizure detection and performance evaluation,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 2, pp. 238–245, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. C. Stöllberger, J. Finsterer, W. Lutz et al., “Multivariate analysis-based prediction rule for pulmonary embolism,” Thrombosis Research, vol. 97, no. 5, pp. 267–273, 2000. View at Publisher · View at Google Scholar · View at Scopus
  8. A. F. Rabbi, A. Aarabi, and R. Fazel-Rezai, “Fuzzy rule-based seizure prediction based on correlation dimension changes in intracranial EEG,” in Proceedings of the IEEE Engineering in Medicine and Biology Society Conference, pp. 3301–3304, 2010.
  9. L. D. Iasemidis, D. S. Shiau, W. Chaovalitwongse et al., “Adaptive epileptic seizure prediction system,” IEEE transactions on bio-medical engineering, vol. 50, no. 5, pp. 616–627, 2003. View at Google Scholar · View at Scopus
  10. D. Liu, Z. Pang, and Z. Wang, “Epileptic seizure prediction by a system of particle filter associated with a neural network,” Eurasip Journal on Advances in Signal Processing, vol. 2009, Article ID 638534, 10 pages, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Shahidi Zandi, G. A. Dumont, M. Javidan, and R. Tafreshi, “An entropy-based approach to predict seizures in temporal lobe epilepsy using scalp EEG,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 228–2231, 2009.
  12. Z. Rogowski, I. Gath, and E. Bental, “On the prediction of epileptic seizures,” Biological Cybernetics, vol. 42, no. 1, pp. 9–15, 1981. View at Google Scholar · View at Scopus
  13. Y. Salant, I. Gath, and O. Henriksen, “Prediction of epileptic seizures from two-channel EEG,” Medical and Biological Engineering and Computing, vol. 36, no. 5, pp. 549–556, 1998. View at Publisher · View at Google Scholar · View at Scopus
  14. M. M. Ali, “Distribution of the least squares estimator in a first-order autoregressive model,” Econometric Reviews, vol. 21, no. 1, pp. 89–119, 1996. View at Google Scholar
  15. K. M. Abadir, “The limiting distribution of the autocorrelation coefficient under a unit root,” Annals of Statistics, vol. 21, no. 2, pp. 1058–1070, 1993. View at Google Scholar
  16. S. Y. Hwang, I. V. Basawa, and T. Y. Kim, “Least squares estimation for critical random coefficient first-order autoregressive processes,” Statistics and Probability Letters, vol. 76, no. 3, pp. 310–317, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. W. A. Fuller, Introduction to Statistical Time Series, Wiley, New York, NY, USA, 2nd edition, 1996.
  18. O. A. Sykes, “An Introduction to Regression Analysis,” in The Inaugural Coase Lecture, University of Chicago, 1988, Chicago Working Paper in Law & Economics. View at Google Scholar
  19. L. S. Liebovitch and T. Toth, “A fast algorithm to determine fractal dimensions by box counting,” Physics Letters A, vol. 141, no. 8-9, pp. 386–390, 1989. View at Google Scholar · View at Scopus
  20. H. O. Peitgen, H. Jurgens, and D. Saupe, Chaos and Fractals: New Frontiers of Science, Springer, New York, NY, USA, 1992.
  21. D. G. Childers, D. P. Skinner, and R. C. Kemerait, “The cepstrum: a guide to processing,” Proceedings of the IEEE, vol. 65, no. 10, pp. 1428–1443, 1977. View at Google Scholar · View at Scopus
  22. D. Chakrabarti and C. Faloutsos, “F4: large-scale automated forecasting using fractals,” in Proceedings of the 11th International Conference on Information and Knowledge Management (CIKM '02), pp. 2–9, November 2002. View at Scopus
  23. B. B. Mandelbrot, The Fractal Geometry of Nature, Freeman, New York, NY, USA, 1983.
  24. M. Sarkar and T. Y. Leong, “Characterization of medical time series using fuzzy similarity-based fractal dimensions,” Artificial Intelligence in Medicine, vol. 27, no. 2, pp. 201–222, 2003. View at Publisher · View at Google Scholar · View at Scopus
  25. F. Fernandex-Rodriguez, S. Sosvilla-Rivero, and J. Andrada-Felix, “Nearest neighbor predictions in foreign exchange markets,” Working Papers from FEDEA, 2002. View at Google Scholar
  26. G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003. View at Publisher · View at Google Scholar · View at Scopus
  27. F. Mormann, T. Kreuz, C. Rieke et al., “On the predictability of epileptic seizures,” Clinical Neurophysiology, vol. 116, no. 3, pp. 569–587, 2005. View at Publisher · View at Google Scholar · View at Scopus
  28. B. Schelter, M. Winterhalder, T. Maiwald et al., “Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction,” Chaos, vol. 16, no. 1, pp. 013108–013110, 2006. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Chávez, M. Le Van Quyen, V. Navarro, M. Baulac, and J. Martinerie, “Spatio-temporal dynamics prior to neocortical seizures: amplitude versus phase couplings,” IEEE Transactions on Bio-Medical Engineering, vol. 50, no. 5, pp. 571–583, 2003. View at Google Scholar · View at Scopus
  30. M. Winterhalder, T. Maiwald, H. U. Voss, R. Aschenbrenner-Scheibe, J. Timmer, and A. Schulze-Bonhage, “The seizure prediction characteristics: a general framework to assess and compare seizure prediction methods,” Epilepsy and Behavior, vol. 4, no. 3, pp. 318–325, 2003. View at Publisher · View at Google Scholar · View at Scopus
  31. X. Li and X. Yao, “Application of fuzzy similarity to prediction of epileptic seizures using EEG signals,” in Proceedings of the 2nd International Confernce on Fuzzy Systems and Knowledge Discovery (FSKD '05), vol. 3613, pp. 645–652, August 2005. View at Scopus
  32. X. Li and G. Ouyang, “Nonlinear similarity analysis for epileptic seizures prediction,” Nonlinear Analysis, Theory, Methods and Applications, vol. 64, no. 8, pp. 1666–1678, 2006. View at Publisher · View at Google Scholar · View at Scopus
  33. S. Gigola, F. Ortiz, C. E. D'Attellis, W. Silva, and S. Kochen, “Prediction of epileptic seizures using accumulated energy in a multiresolution framework,” Journal of Neuroscience Methods, vol. 138, no. 1-2, pp. 107–111, 2004. View at Publisher · View at Google Scholar · View at Scopus
  34. T. Maiwald, M. Winterhalder, R. Aschenbrenner-Scheibe, H. U. Voss, A. Schulze-Bonhage, and J. Timmer, “Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic,” Physica D, vol. 194, no. 3-4, pp. 357–368, 2004. View at Publisher · View at Google Scholar · View at Scopus
  35. J. Z. Liu, L. D. Zhang, and G. H. Yue, “Fractal dimension in human cerebellum measured by magnetic resonance imaging,” Biophysical Journal, vol. 85, no. 6, pp. 4041–4046, 2003. View at Google Scholar · View at Scopus
  36. R. Esteller, J. Echauz, B. Pless, T. Tcheng, and B. Litt, “Real-time simulation of a seizure detection system suitable for an implantable device,” Epilepsia, vol. 43, supplement 7, p. 46, 2002. View at Google Scholar
  37. J. C. Sackellares, L. D. Iasemidis, R. L. Gilmore et al., “Epilepsy—when chaos fails,” in Chaos in the Brain, pp. 112–1133, World Scientific, 2000. View at Google Scholar