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An Application of Tucker Decomposition for Detecting Epilepsy EEG signals

  • Received : 2015.06.10
  • Accepted : 2015.08.10
  • Published : 2015.06.30

Abstract

Epileptic Seizure is a popular brain disease in the world. It affects the nervous system and the activities of brain function that make a person who has seizure signs cannot control and predict his actions. Based on the Electroencephalography (EEG) signals which are recorded from human or animal brains, the scientists use many methods to detect and recognize the abnormal activities of brain. Tucker model is investigated to solve this problem. Tucker decomposition is known as a higher-order form of Singular Value Decomposition (SVD), a well-known algorithm for decomposing a matric. It is widely used to extract good features of a tensor. After decomposing, the result of Tucker decomposition is a core tensor and some factor matrices along each mode. This core tensor contains a number of the best information of original data. In this paper, we used Tucker decomposition as a way to obtain good features. Training data is primarily applied into the core tensor and the remained matrices will be combined with the test data to build the Tucker base that is used for testing. Using core tensor makes the process simpler and obtains higher accuracies.

Keywords

References

  1. S. Sanei and J. Chambers EEG signal processing, Wiley, pp. 161-191, 2007.
  2. L. D. Iasemidis, "Epileptic seizure prediction and control," IEEE trans. Biomed Engng., Vol. 50, No. 5, pp.549-558, May 2003. https://doi.org/10.1109/TBME.2003.810705
  3. J. F. Annegers, "The epidemiology of epilepsy," The treatment of Epilepsy, pp. 157-164, 1993.
  4. T. G. Kolda and B. W. Bader, "Tensor Decomposition and Applications", Society for Industrial and Applied Mathematics, Vol. 51, No. 3, pp. 455-500, Jun. 2008.
  5. L. De Lathauwer, B. De Moor, and J. Vandewalle, "A multilinear singular value decomposition," SIAM Journal on Matrix Analysis and Applications, Vol. 21, No. 4, pp. 1253-1278, Mar.-May 2000 https://doi.org/10.1137/S0895479896305696
  6. M. A. O. Vasilescu and D. Terzopoulos, "Multilinear analysis of image ensembles: Tensor face," 7th European Conference on Computer Vision, pp. 447-460, May 2002.
  7. L. R. Tucker "Some mathematical notes on threemode factor analysis," Psychometrika, Vol. 31, No. 3, pp. 279-311, Sep. 1966. https://doi.org/10.1007/BF02289464
  8. P. M. Kroonenberg and J. De Leeuw, "Principal component analysis of three-mode data by means of alternating least squares algorithms," Psychometrika, Vol. 45, No. 1, pp. 69-97, Mar. 1980. https://doi.org/10.1007/BF02293599
  9. A. H. Phan and A. Cichocki, "Tensor decompositions for feature extraction and classification of high dimensional datasets," Nonlinear theory and its applications, IEICE, Vol.1, No. 1, pp. 37-68, 2010 https://doi.org/10.1587/nolta.1.37
  10. M. van Gerven, "Tensor decomposition for probabilistic classification," Intelligent Data Analysis in bioMedicine and Pharmacology(IDAMAP), pp.25-26, Jul. 2007
  11. The International Epilepsy Electrophysiology portal: https://www.ieeg.org/
  12. Upenn and Mayo Clinic's Seizure Detection Challenge: https://www.kaggle.com/c/seizure-detection
  13. A. Shoeb and J. Guttag, "Application of Machine Learning To Epilepsy Seizure Detection," Proceedings of the 27th International Conference on Machine Learning, pp. 975-982, Jun. 2010
  14. R. A. Johnson and D. W. Wichern: Applied Multivariate Statistical Analysis, Sixth Edition, Pearson, 2007.
  15. Z. Xu, F. Yang, and Y. Qi, "Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway data Analysis," Proceedings of the 29 th International Conference on Machine Learning, pp.1023-1030, Jan. 2012
  16. B. Chan, Z. Li, and S. Zhang, "On Tensor Tucker decomposition: the case for an adjustable core size," Technical report, 2013.
  17. A. Cichocki, D. mandic, A-H Phan, C. Caiafa, G. Zhou, Q. Zhao, and L. De Lathauwer, "Tensor decomposition for Signal Processing Applications," Signal Processing Magazine, Vol. 32, No.2, pp. 145-163, Mar. 2015
  18. Wikipedia, http://en.wikipedia.org/wiki
  19. N. Renard and S. Bourennane, "Dimensionality reduction based on tensor modeling for classification methods," IEEE Trans. Geosci. Remote Sens., Vol. 47, No. 4, pp. 1123-1131, Apr. 2009. https://doi.org/10.1109/TGRS.2008.2008903
  20. A. Cichocki, R. Zdunek, and A. H. Phan, and S. AMari, Nonnegative Matrix and Tensor Factorizations, Wiley, 2009.
  21. A. Karami, M. Yazdi, and G. Mercier, "Compression of Hyperspectral Images Using Discrete Wavelet transform and Tucker decomposition," IEEE Journal of selected topics in applied earth observations and remote sensing, Vol. 5, No. 2, pp. 444-450, Mar. 2012. https://doi.org/10.1109/JSTARS.2012.2189200
  22. N. Kalouptsidis, "Signal Processing Systems, theory and design," John Wiley & Sons, INC, 1997.
  23. R. Q. Quiroga "Quantitative analysis of EEG signals: Time-frequency methods and Chaos theory," Ph. D. thesis, Institute of Signal Processing and Institute Physiology, Medical University of Lubeck, Germany, 1998.
  24. F. L. da Silva and D. L. Schomer, "Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields," Lippincott Williams & Wilkins, 2011.