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The Scientific World Journal
Volume 2013 (2013), Article ID 896056, 10 pages
http://dx.doi.org/10.1155/2013/896056
Research Article

A Hierarchical Method for Removal of Baseline Drift from Biomedical Signals: Application in ECG Analysis

1Department of Computer Science, School of Engineering, Virginia Commonwealth University, 401 West Main Street, Richmond, VA 23284, USA
2Department of Electrical and Computer Engineering, School of Engineering, Virginia Commonwealth University, 401 West Main Street, Richmond, VA 23284, USA
3Department of Biomedical Engineering, School of Engineering, Virginia Commonwealth University, 401 West Main Street, Richmond, VA 23284, USA
4Department of Emergency Medicine and Michigan Critical Injury and Illness Research Center, University of Michigan, Ann Arbor, MI 48109, USA

Received 12 February 2013; Accepted 9 April 2013

Academic Editors: G. Koch, J. Ma, and V. Positano

Copyright © 2013 Yurong Luo 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. L. T. Sheffield, A. Berson, D. Bragg-Remschel et al., “Recommendations for standard of instrumentation and practice in the use of ambulatory electrocardiography,” Circulation, vol. 71, no. 3, pp. 626A–636A, 1985. View at Scopus
  2. J. A. van Alsté, W. van Eck, and O. E. Herrmann, “ECG baseline wander reduction using linear phase filters,” Computers and Biomedical Research, vol. 19, no. 5, pp. 417–427, 1986. View at Scopus
  3. L. P. Harting, N. M. Fedotov, and C. H. Slump, “On baseline drift suppressing in ECG-recordings,” in Proceedings of the IEEE Benelux Signal Processing Symposium, pp. 133–136, 2004.
  4. L. Sornmo, “Time-varying digital filtering of ECG baseline wander,” Medical and Biological Engineering and Computing, vol. 31, no. 5, pp. 503–508, 1993. View at Publisher · View at Google Scholar · View at Scopus
  5. C. R. Meyer and H. N. Keiser, “Electrocardiogram baseline noise estimation and removal using cubic splines and state-space computation techniques,” Computers and Biomedical Research, vol. 10, no. 5, pp. 459–470, 1977. View at Publisher · View at Google Scholar · View at Scopus
  6. C. Papaloukas, D. I. Fotiadis, A. P. Liavas, A. Likas, and L. K. Michalis, “A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms,” Medical and Biological Engineering and Computing, vol. 39, no. 1, pp. 105–112, 2001. View at Scopus
  7. V. S. Chouhan and S. S. Mehta, “Total removal of baseline drift from ECG signal,” in Proceedings of International Conference on Computing: Theory and Applications (ICCTA '07), pp. 512–515, March 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. K. L. Park, K. J. Lee, and H. R. Yoon, “Application of a wavelet adaptive filter to minimise distortion of the ST-segment,” Medical and Biological Engineering and Computing, vol. 36, no. 5, pp. 581–586, 1998. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Blanco-Velasco, B. Weng, and K. E. Barner, “ECG signal denoising and baseline wander correction based on the empirical mode decomposition,” Computers in Biology and Medicine, vol. 38, no. 1, pp. 1–13, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Raimon, P. Laguna, N. V. Thakor, and P. Caminal, “Adaptive baseline wander removal in the ECG: comparative analysis with cubic spline technique,” in Proceeding of Computers in Cardiology, pp. 143–146, October 1992.
  11. Z. Barati and A. Ayatollahi, “Baseline wandering removal by using independent component analysis to single-channel ECG data,” in Proceedings of International Conference on Biomedical and Pharmaceutical Engineering (ICBPE '06), pp. 152–156, December 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. M. P. S. Chawla, H. K. Verma, and V. Kumar, “Artifacts and noise removal in electrocardiograms using independent component analysis,” International Journal of Cardiology, vol. 129, no. 2, pp. 278–281, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. M. P. S. Chawla, H. K. Verma, and V. Kumar, “Independent component analysis: a novel technique for removal of artifacts and baseline wander in ECG,” in Proceedings of the 3rd National Control Instrumentation System Conference, pp. 14–18, 2006.
  14. M. Keralapura, M. Pourfathi, and B. Sirkeci-Mergen, “Impact of contrast functions in Fast-ICA on twin ECG separation,” IAENG International Journal of Computer Science, vol. 38, no. 1, pp. 38–47, 2011. View at Scopus
  15. K. Arfanakis, D. Cordes, V. M. Haughton, C. H. Moritz, M. A. Quigley, and M. E. Meyerand, “Combining independent component analysis and correlation analysis to probe interregional connectivity in fMRI task activation datasets,” Magnetic Resonance Imaging, vol. 18, no. 8, pp. 921–930, 2000. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. Ye, Z. L. Zhang, J. Zeng, and L. Peng, “A fast and adaptive ICA algorithm with its application to fetal electrocardiogram extraction,” Applied Mathematics and Computation, vol. 205, no. 2, pp. 799–806, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Hyvärinen and E. Oja, “Independent component analysis: algorithms and applications,” Neural Networks, vol. 13, no. 4-5, pp. 411–430, 2000. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Milanesi, N. Vanello, V. Positano et al., “Frequency domain approach to blind source separation in ECG monitoring by wearable system,” in Proceedings of Computers in Cardiology, pp. 767–770, September 2005. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Milanesi, N. Vanello, V. Positano, M. F. Santarelli, and L. Landini, “Separation and identification of biomedical signals based on frequency domain independent component analysis,” WSEAS Transactions on Systems, vol. 4, no. 10, pp. 1752–1761, 2005. View at Scopus
  20. A. Hyvarinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Transactions on Neural Networks, vol. 10, no. 3, pp. 626–634, 1999. View at Publisher · View at Google Scholar · View at Scopus
  21. C. J. James and O. J. Gibson, “Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 9, pp. 1108–1116, 2003. View at Publisher · View at Google Scholar · View at Scopus
  22. J. Lee, K. L. Park, and K. J. Lee, “Temporally constrained ICA-based foetal ECG separation,” Electronics Letters, vol. 41, no. 21, pp. 1158–1160, 2005. View at Publisher · View at Google Scholar · View at Scopus
  23. A. Hyvärinen, P. O. Hoyer, and M. Inki, “Topographic independent component analysis,” Neural Computation, vol. 13, no. 7, pp. 1527–1558, 2001. View at Publisher · View at Google Scholar · View at Scopus
  24. V. D. Calhoun, T. Adali, G. D. Pearlson, and J. J. Pekar, “Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms,” Human Brain Mapping, vol. 13, no. 1, pp. 43–53, 2001. View at Publisher · View at Google Scholar · View at Scopus
  25. B. Widrow, J. R. Glover, J. M. McCool, et al., “Adaptive noise cancelling: principles and applications,” Proceedings of the IEEE, vol. 63, no. 12, pp. 1692–1716, 1975. View at Scopus
  26. J. R. Glover, “Adaptive noise canceling applied to sinusoidal interferences,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 25, no. 6, pp. 484–491, 1977. View at Scopus
  27. A. Hyvarinen, “New approximations of differential entropy for independent component analysis and projection pursuit,” in Advances in Neural Information Processing Systems, vol. 10, pp. 273–279, 1998.
  28. S. Y. Ji, A. Belle, K. R. Ward et al., “Heart rate variability analysis during central hypovolemia using wavelet transformation,” Journal of Clinical Monitoring and Computing, pp. 1–14, 2013.
  29. W. S. Cleveland, “Robust locally weighted regression and smoothing scatterplots,” Journal of the American Statistical Assocaition, vol. 74, pp. 829–836, 1979. View at Publisher · View at Google Scholar