Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 217067, 8 pages
http://dx.doi.org/10.1155/2014/217067
Research Article

An Effective Way of J Wave Separation Based on Multilayer NMF

College of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, China

Received 20 June 2014; Revised 25 August 2014; Accepted 1 September 2014; Published 12 October 2014

Academic Editor: Irena Cosic

Copyright © 2014 Deng-ao Li 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. X. Yang and X. Li, “J wave research history and status quo,” China Cardiac Pacemaker and Electrophysiology, vol. 13, no. 1, pp. 50–51, 1999. View at Google Scholar
  2. G. X. Yan, “Abnormal J wave syndrome,” Journal of Clinical Electrocardiology, vol. 1, pp. 3–9, 2007. View at Google Scholar
  3. G. X. Yan, Q. H. Yao, D. Q. Wang, and C. C. Cui, “Electrocardiographic J wave and J wave syndromes,” Chinese Journal of Cardiac Arrhythmias, vol. 8, no. 6, pp. 360–365, 2004. View at Google Scholar
  4. Z. Zhao, H. Lu, and C. Xu, “An blind source separation algorithm based on constrained NMF,” Piezoelectrics and Acoustooptics, vol. 32, no. 6, pp. 1049–1052, 2010. View at Google Scholar · View at Scopus
  5. F. Potet, P. Mabo, G. le Coq et al., “Novel Brugada SCN5A mutation leading to ST segment elevation in the inferior or the right precordial leads,” Journal of Cardiovascular Electrophysiology, vol. 14, no. 2, pp. 200–203, 2003. View at Publisher · View at Google Scholar · View at Scopus
  6. M. D. Plumbley, “Algorithms for nonnegative independent component analysis,” IEEE Transactions on Neural Networks, vol. 14, no. 3, pp. 534–543, 2003. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Cichocki, R. Zdunek, and S.-I. Amari, “New algorithms for non-negative matrix factorization in applications to blind source separation,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '06), vol. 5, pp. V621–V624, Toulouse, France, May 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Pascual-Montano, J. M. Carazo, K. Kochi, D. Lehmann, and R. D. Pascual-Marqui, “Nonsmooth nonnegative matrix factorization (nsNMF),” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 403–415, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. F.-Y. Wang, Y. Wang, T.-H. Chan, and C.-Y. Chi, “Blind separation of multichannel biomedical image patterns by non-negative least-correlated component analysis,” in Pattern Recognition in Bioinformatics, vol. 4146 of Lecture Notes in Computer Science, pp. 151–162, 2006. View at Google Scholar
  10. W. S. Ouedraogo, A. Souloumiac, M. Jaïdane, and C. Jutten, “Non-negative blind source separation algorithm based on minimum aperture simplicial cone,” IEEE Transactions on Signal Processing, vol. 62, no. 2, pp. 376–389, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. P. O. Hoyer, “Non-negative matrix factorization with sparseness constraints,” Journal of Machine Learning Research, vol. 5, pp. 1457–1469, 2004. View at Google Scholar · View at MathSciNet
  12. Y. Luo, R. H. Hargraves, A. Belle et al., “A hierarchical method for removal of baseline drift from biomedical signals: application in ECG analysis,” The Scientific World Journal, vol. 2013, Article ID 896056, 10 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Cichocki and R. Zdunek, “Multilayer nonnegative matrix factorisation,” Electronics Letters, vol. 42, no. 16, pp. 947–948, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. A. Cichocki and R. Zdunek, “Multilayer nonnegative matrix factorization using projected gradient approaches,” International Journal of Neural Systems, vol. 17, no. 6, pp. 431–446, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Matsuda and K. Yamaguchi, “Linear multilayer ica using adaptive pca,” Neural Processing Letters, vol. 30, no. 2, pp. 133–144, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. D. Li, J. Zhao, H. Liu, and D. Hao, “The application of FastICA combined with related function in blind signal separation,” Mathematical Problems in Engineering, vol. 2014, Article ID 953745, 9 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus