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Reseach Article

Denoising Baseline Wander Noise from Electrocardiogram Signal using Fast ICA with Multiple Adjustments

by Nevi Jain, Devendra Kumar Shakya
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 99 - Number 2
Year of Publication: 2014
Authors: Nevi Jain, Devendra Kumar Shakya
10.5120/17348-7691

Nevi Jain, Devendra Kumar Shakya . Denoising Baseline Wander Noise from Electrocardiogram Signal using Fast ICA with Multiple Adjustments. International Journal of Computer Applications. 99, 2 ( August 2014), 34-39. DOI=10.5120/17348-7691

@article{ 10.5120/17348-7691,
author = { Nevi Jain, Devendra Kumar Shakya },
title = { Denoising Baseline Wander Noise from Electrocardiogram Signal using Fast ICA with Multiple Adjustments },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 99 },
number = { 2 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 34-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume99/number2/17348-7691/ },
doi = { 10.5120/17348-7691 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:27:10.117250+05:30
%A Nevi Jain
%A Devendra Kumar Shakya
%T Denoising Baseline Wander Noise from Electrocardiogram Signal using Fast ICA with Multiple Adjustments
%J International Journal of Computer Applications
%@ 0975-8887
%V 99
%N 2
%P 34-39
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The electrocardiogram (ECG) is widely utilitarian for prognostic of heart diseases. Quality and utilization of ECG signal is affected by different noises and hence it is very difficult to measure important parameter to know the exact condition of heart. Baseline wander is one type of noise which is normally seen in ECG signal. This artifact severally limits the usefulness of recorded ECG signals and thus need to be removed for better clinical appraisal. Independent component analysis (ICA) is a statistical technique for estimating a multidimensional random vector into components that are statistically not dependent from each other. This paper proposed the implementation of fast ICA with multiple adjustments for removing baseline wander noise effect from ECG. Simulation results demonstrate that the proposed method is better in denoised the baseline wander noise from ECG signal.

References
  1. Joshi, S. L. , Vatti R. A. , and Tornekar, R. V. 2013. A Survey on ECG Signal Denoising Techniques. International Conference on Communication Systems and Network Technologies. 60-64.
  2. Das, M. K. , and Ari, S. 2013. Analysis of ECG signal denoising method based on S-transform. IRBM. Vol. 34. Issue 6. 362–37.
  3. Gurumurthy, S. , and Valarmozhi. 2013. System Design for Baseline Wander Removal of ECG Signals with Empirical Mode Decomposition Using Matlab. International Journal of Soft Computing and Engineering (IJSCE). Vol. 3. Issue 3.
  4. Jane, R. , Laguna, P. , Thakor, N. V. , and Caminal, P. 1992. Adaptive baseline wander removal in the ECG: comparative analysis with cubic spline technique. Proceedings of Computers in Cardiology, Durham, NC. 143-146.
  5. Chouhan, V. S. , and Mehta, S. S. 2007. Total removal of baseline drift from ECG signal. In Proceedings of International Conference on Computing: Theory and Applications (ICCTA '07). 512–515.
  6. Van Alst´e, J. A. , Van Eck, W. , and Herrmann, O. E. 1986. ECG baseline wander reduction using linear phase filters. Computers and Biomedical Research. Vol. 19. No. 5. 417–427.
  7. Kaur, M. and Seema. 2011. Comparisons of Different Approaches for Removal of Baseline Wander from ECG Signal. International Journal of Computer Applications (IJCA). Vol. 5. 30-34.
  8. Harting, L. P. , Fedotov, N. M. , and Slump, C. H. 2004. On baseline drift suppressing in ECG-recordings. In Proceedings of the IEEE Benelux Signal Processing Symposium. 133–136.
  9. Hyvärinen, A. , and Oja, E. 2000. Independent Component Analysis: Algorithms and Applications. In Neural Network. Vol. 13. Issues 4–5. 411–430.
  10. Meyer, C. R. , and Keiser, H. N. 1977. Electrocardiogram baseline noise estimation and removal using cubic splines and state-space computation techniques. In Computers and Biomedical Research. Vol. 10. No. 5. 459–470.
  11. Park, K. L. , Lee, K. J. , and Yoon, H. R. 1998. Application of a wavelet adaptive filter to minimize distortion of the ST-Segment. Medical and Biological Engineering and Computing. Vol. 36. 581-586.
  12. Blanco-Velasco, M. , Weng, B. , and Barner, K. E. 2008. ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Computers in Biology and Medicine. Vol. 38. 1-13.
  13. Kabir, Md. A. , and Shahnaz, C. 2012. Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomedical Signal Processing and Control. Vol. 7. 481– 489.
  14. Laguna, P. , Jane, R. , and Caminal, P. 1992. Adaptive Filtering of ECG Baseline Wander. Engineering in Medicine and Biology Society, International Conference of the IEEE. Vol. 14.
  15. Widrow, B. , Glover, J. R. , and McCool, J. M. 1975. Adaptive noise cancelling: principles and applications. Proceedings of the IEEE. Vol. 63. No. 12. 1692–1716.
  16. Barati, Z. , and Ayatollahi, A. 2006. Baseline wandering removal by using independent component analysis to single-channel ECG data. Proceedings of International Conference on Biomedical and Pharmaceutical Engineering (ICBPE '06). 152–156.
  17. Nimitha, U. and Supriya, P. 2011. Independent Component Approach for the Analysis of ECG Signals. Proceedings of the IEEE. 1-5.
  18. Milanesi, M. , Vanello, N. and Positano, V. 2005. Frequency domain approach to blind source separation in ECG monitoring by wearable system. in Proceedings of Computers in Cardiology. 767–770.
  19. Luo, Y. , Hargraves, R. H. , Belle, A. , Bai, O. , Qi, X. , Ward, K. R. , Pfaffenberger, M. P. , and Najarian, K. 2013. A hierarchical method for removal of baseline drift from biomedical signals: application in ECG analysis. The Scientific World Journal. 1-10.
  20. Hyvarinen. Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks. Vol. 10. No. 3. 626–634.
  21. US Patent, ''US5318036'', Hewlett Packard.
Index Terms

Computer Science
Information Sciences

Keywords

Electrocardiogram Baseline Wander Noise Variable Notch Filter Independent Component Analysis.