Open Access
MATEC Web of Conferences
Volume 44, 2016
2016 International Conference on Electronic, Information and Computer Engineering
Article Number 01026
Number of page(s) 9
Section Computer, Algorithm, Control and Application Engineering
Published online 08 March 2016
  1. Hasan, M. A., et al.(2009). Detection and processing techniques of FECG signal for fetal monitoring’,Biological procedures online.11:263–295.
  2. Sameni, Reza, and Gari D. Clifford. (2010). A review of fetal ECG signal processing; issues and promising directions. The open pacing, electrophysiology & therapy journal.3:4–20.
  3. Gao P, Chang E C, Wyse L.(2003). Blind separation of fetal ECG from single mixture using SVD and ICA. In Information, Communications and Signal Processing, 2003 and Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint Conference of the Fourth International Conference on IEEE. 3:1418–1422.
  4. Azzerboni, B., La Foresta, F., Mammone, N., & Morabito, F. C.(2005). A new approach based on wavelet-ICA algorithms for fetal electrocardiogram extraction. ESANN. 193–198.
  5. La Foresta, F., N. Mammone, and F. C. Morabito.(2005). Independent component and wavelet analysis for fECG extraction: the stwaveform evaluation. Computational Intelligence for Measurement Systems and Applications, CIMSA. 2005 IEEE International Conference on. IEEE. 86–90.
  6. Mijovic, Bogdan, et al.(2010). Source separation from single-channel recordings by combining empirical mode decomposition and independent component analysis. Biomedical Engineering, IEEE Transactions on. 57:2188–2196.
  7. De Lathauwer L, De Moor B, Vandewalle J. (2000). Fetal electrocardiogram extraction by blind source subspace separation. IEEE transactions on biomedical engineering, 47:567–572. [CrossRef]
  8. Luenberger, David. (1979). Introduction to dynamic systems: theory, models, and applications. John Wiley & Sons. New York.
  9. Huang N E, Shen Z, Long S R, et al.(1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.454:903–995
  10. Rilling G, Flandrin, P, Goncalves, P.(2003). On empirical mode decomposition and its algorithms. IEEE-EURASIP workshop on nonlinear signal and imageprocessing, NSIP-03, Grado (I). 3: 8–11
  11. Xu, Z., B. Huang, and F. Zhang. (2009). Envelope approach based on special knots for empirical mode decomposition. Electronics letters. 45:480–481. [CrossRef]
  12. Wang L, Yin, J H, Chen, T Q. (2007). Frequency domain bootstrap method for source number estimation of wideband signals. CHINESE JOURNAL OF RADIO SCIENCE. 22:130–132
  13. Zhang, Y F, Guan, J, Wang, J et al.(2006). A Novel Method for Determination of the Number of Signal Sources Based on Hough Transform. Journal of Detection & Control.28:26–28.
  14. Ye, Z F, Xiang, L, Xu, X.(2007). Improvement of source number estimation based on information theoretic criteria. CHINESE JOURNAL OF RADIO SCIENCE. 22:593–598.
  15. Gu, Q W, Jin, W D, Yu, Z B.(2014). Blind source separation of single channel train signal based on EEMD and ICA. Application Research of Computers. 31:1551–1553.
  16. Feng, J Z, Song, L, Huo, X M, Yang, X K, Zhang, W J. (2014). An Optimized Pixel-Wise Weighting Approach for Patch-Based Image Denoising. IEEE Signal Processing Letter. 22:115–119. [CrossRef]
  17. Lee, D D, Seung, H S.(1999). Learning the parts of objects by non-negative matrix factorization. Nature. 401: 788–791 [NASA ADS] [CrossRef] [PubMed]
  18. Schachtner, Poppel, G, Tom, A M, et al.(2009). Minimum determinant constraint for non-negative matrix factorization. Lecture Notes in Computer Science. 5441:106–113. [CrossRef]
  19. Y Zhang, Y Fang. (2007). A NMF algorithm for blind separation of uncorrelated signals. Wavelet Analysis and Pattern Recognition, 2007, ICWAPR’07. International Conference on. IEEE. 3:999–1003 [CrossRef]
  20. Goldberger, AL, Amaral, LAN, Glass, L, Hausolorff, JM, et al. (2000). Components of a New Research Resource for Complex Physuologic Signals, Circulation 101(23):e215–e220 [Circulation Electronic Pages;].

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.