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Journal of Healthcare Engineering
Volume 2018 (2018), Article ID 7901502, 14 pages
https://doi.org/10.1155/2018/7901502
Research Article

Enhancement of the Comb Filtering Selectivity Using Iterative Moving Average for Periodic Waveform and Harmonic Elimination

1Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, Netherlands
2Philips Research Laboratories, Eindhoven, Prof. Holstlaan 4, 5656 AE Eindhoven, Netherlands

Correspondence should be addressed to José L. Ferreira; ln.eut@arierref.l.j

Received 10 August 2017; Accepted 28 November 2017; Published 1 February 2018

Academic Editor: Saugat Bhattacharyya

Copyright © 2018 José L. Ferreira 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. R. M. Rangayyan, Biomedical Signal Analysis: A Case-Study Approach, Wiley, New York, 2002.
  2. S. Braun, “The synchronous (time domain) average revisited,” Mechanical Systems and Signal Processing, vol. 25, no. 4, pp. 1087–1102, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Meher, P. Meher, and G. Dash, “Improved comb filter based approach for effective prediction of protein coding regions in DNA sequences,” Journal of Signal and Information Processing, vol. 2, no. 02, pp. 88–99, 2011. View at Publisher · View at Google Scholar
  4. J. G. Proakis and D. G. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, Prentice-Hall, Upper Saddle River, 3rd edition, 1996.
  5. Z. Zeng, Y. Xie, Y. Wang, Y. Guan, L. Li, and X. Zhang, “An improved harmonic current detection method based on parallel active power filter,” in Proceedings of the 2nd Asia Conference on Power and Electrical Engineering, ACPEE 2017, pp. 1–6, IOP Publishing, Shanghai, China, March 2017.
  6. M. Tahir and S. K. Mazumder, “Improving dynamic response of active harmonic compensator using digital comb filter,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 2, no. 4, pp. 994–1002, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Prodić, J. Chen, D. Maksimović, and R. W. Erickson, “Self-tuning digitally controlled low-harmonic rectifier having fast dynamic response,” IEEE Transactions on Power Electronics, vol. 18, no. 1, pp. 420–428, 2003. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Braun, “The extraction of periodic waveforms by time domain averaging,” Acustica, vol. 32, no. 2, pp. 69–77, 1975. View at Google Scholar
  9. P. D. McFadden, “A revised model for the extraction of periodic waveforms by time domain averaging,” Mechanical Systems and Signal Processing, vol. 1, no. 1, pp. 83–95, 1987. View at Publisher · View at Google Scholar · View at Scopus
  10. P. J. Allen, O. Josephs, and R. Turner, “A method for removing imaging artifact from continuous EEG recorded during functional MRI,” NeuroImage, vol. 12, no. 2, pp. 230–239, 2000. View at Publisher · View at Google Scholar · View at Scopus
  11. R. Becker, P. Ritter, M. Moosmann, and A. Villringer, “Visual evoked potentials recovered from fMRI scan periods,” Human Brain Mapping, vol. 26, no. 3, pp. 221–230, 2005. View at Publisher · View at Google Scholar · View at Scopus
  12. K. Anami, T. Mori, F. Tanaka et al., “Stepping stone sampling for retrieving artifact-free electroencephalogram during functional magnetic resonance imaging,” NeuroImage, vol. 19, no. 2, pp. 281–295, 2003. View at Publisher · View at Google Scholar · View at Scopus
  13. W. X. Yan, K. J. Mullinger, M. J. Brookes, and R. Bowtell, “Understanding gradient artefacts in simultaneous EEG/fMRI,” NeuroImage, vol. 46, no. 2, pp. 459–471, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. H. Mandelkow, P. Halder, P. Boesiger, and D. Brandeis, “Synchronisation facilitates removal of MRI artefacts from concurrent EEG recordings and increases usable bandwidth,” NeuroImage, vol. 32, no. 3, pp. 1120–1126, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. G. S. Spencer, EEG-fMRI: Novel Methods for Gradient Artefact Correction [Ph.D. Thesis], University of Nottingham, Nottingham, UK, 2015.
  16. L. Lin, Y. Wang, and H. Zhou, “Iterative filtering as an alternative algorithm for empirical mode decomposition,” Advances in Adaptive Data Analysis, vol. 1, no. 4, pp. 543–560, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Wang, G.-W. Wei, and S. Yang, “Iterative filtering decomposition based on local spectral evolution kernel,” Journal of Scientific Computing, vol. 50, no. 3, pp. 629–664, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998. View at Publisher · View at Google Scholar
  19. J. L. Ferreira, R. M. Aarts, and P. J. M. Cluitmans, “Optimized moving-average filtering for gradient artefact correction during simultaneous EEG-fMRI,” in Proceedings of the 5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC), pp. 1–6, IEEE, Salvador, Brazil, May 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. J. L. Ferreira, Y. Wu, R. M. H. Besseling, R. Lamerichs, and R. M. Aarts, “Gradient artefact correction and evaluation of the EEG recorded simultaneously with fMRI data using optimised moving-average,” Journal of Medical Engineering, vol. 2016, Article ID 9614323, 17 pages, 2016. View at Publisher · View at Google Scholar
  21. M. Vlček and P. Zahradník, “Fast analytical design algorithms for FIR notch filters,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 51, no. 3, pp. 608–623, 2004. View at Publisher · View at Google Scholar · View at Scopus
  22. S. Sanei and J. A. Chambers, EEG Signal Processing, Wiley, Hoboken, 2007. View at Publisher · View at Google Scholar · View at Scopus