http://iet.metastore.ingenta.com
1887

Electrocardiogram signal denoising by clustering and soft thresholding

Electrocardiogram signal denoising by clustering and soft thresholding

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Signal Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Separating signal from unwanted noise is a major problem when analysing biomedical data, such as electrocardiography. Electrocardiogram (ECG) data are typically a mixture of real signal and various sources of noise, including baseline wander, power line interference, and electromagnetic interference. Since ECG signals are non-stationary physiological signals, the wavelet transform has been proposed to be an effective tool for eliminating unwanted noise from the ECG data. Here, the authors proposed a new noise reduction method for ECG data based on the discrete wavelet transform and hidden Markov model. They performed Monte Carlo simulations to compare the performance of this new method with seven other well-known denoising techniques.

References

    1. 1)
      • 1. Elhaj, F.A., Salim, N., et al: ‘Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals’, Comput. Methods Programs Biomed., 2016, 127, pp. 5263.
    2. 2)
      • 2. Chazal de, P., Dwyer, M.O., et al: ‘Automatic classification of heartbeats using ECG morphology and heartbeat interval features’, IEEE Trans. Biomed. Eng., 2004, 51, (7), pp. 11961206.
    3. 3)
      • 3. Kim, D.H., Oh, H.S.: ‘Hierarchical smoothing technique by empirical mode decomposition’, Korean J. Appl. Stat., 2006, 19, (2), pp. 319330.
    4. 4)
      • 4. Knight, M.I., Nason, G.P.: ‘A non-decimated lifting transform’, Stat. Comput., 2009, 19, (1), pp. 116.
    5. 5)
      • 5. Condat, L.: ‘A direct algorithm for 1d total variation denoising’, IEEE Signal Process. Lett., 2013, 20, (1), pp. 10541057.
    6. 6)
      • 6. Percival, D.B., Walden, A.T.: ‘Wavelet methods for time series analysis’ (Cambridge University Press, Cambridge, 2006), Vol. 4.
    7. 7)
      • 7. Donoho, D.L., Johnstone, I.M.: ‘Adapting to unknown smoothness via wavelet shrinkage’, J. Am. Stat. Assoc., 1995, 90, (432), pp. 12001224.
    8. 8)
      • 8. Han, G., Xu, Z.: ‘Electrocardiogram signal denoising based on a new improved wavelet thresholding’, Rev. Sci. Instrum., 2016, 87, (8), p. 084303.
    9. 9)
      • 9. Chipman, H.A., Kolaczyk, E.D., McCulloch, R.E.: ‘Adaptive Bayesian wavelet shrinkage’, J. Am. Stat. Assoc., 1997, 92, (440), pp. 14131421.
    10. 10)
      • 10. Li, J., Zhang, Y.: ‘An improved Bayesian wavelet shrinkage denoising’, Tech. Autom. Appl., 2012, 1, p. 019.
    11. 11)
      • 11. Borran, M.J., Nowak, R.D.: ‘Wavelet-based denoising using hidden Markov models’, Acoust. Speech Signal Process., 2001, 6, pp. 39253928.
    12. 12)
      • 12. Crouse, M.S., Nowak, R.D., Baraniuk, R.G.: ‘Wavelet-based statistical signal processing using hidden Markov models’, IEEE Trans. Signal Process., 1998, 46, (4), pp. 886902.
    13. 13)
      • 13. Romberg, J.K., Choi, H., Baraniuk, R.G.: ‘Bayesian tree-structured image modeling using wavelet-domain hidden Markov models’, IEEE Trans. Image Process., 2001, 10, (7), pp. 10561068.
    14. 14)
      • 14. Su, T., Zhang, D.F., Bi, D.Y.: ‘Denoising method based on wavelet-domain classified hidden Markov tree model’, Infrared Laser Eng., 2005, 34, (2), pp. 232235.
    15. 15)
      • 15. Lahmiri, S.: ‘Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains’, Healthc. Technol. Lett., 2014, 1, (3), pp. 104109.
    16. 16)
      • 16. Lahmiri, S., Boukadoum, M.: ‘Physiological signal denoising with variational mode decomposition and weighted reconstruction after DWT thresholding’. IEEE Int. Symp. on Circuits and Systems (ISCAS), Lisbon, Portugal, 2015, pp. 806809.
    17. 17)
      • 17. Lahmiri, S., Boukadoum, M.: ‘A weighted bio-signal denoising approach using empirical mode decomposition’, Biomed. Eng. Lett., 2015, 5, (2), pp. 131139.
    18. 18)
      • 18. Rakshit, M., Das, S.: ‘An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter’, Biomed. Signal Proc. Control, 2018, 40, pp. 140148.
    19. 19)
      • 19. Zucchini, W., Macdonald, I.L.: ‘Hidden Markov models for time series: an introduction using R’ (CRC Press, Boca Raton, 2009).
    20. 20)
      • 20. Meyer, Y.: ‘Wavelets: algorithms and applications’ (SIAM, Philadelphia, Pennsylvania, USA, 1993).
    21. 21)
      • 21. Stein, C.M.: ‘Estimation of the mean of a multivariate normal distribution’, Ann. Stat., 1981, 9, pp. 11351151.
    22. 22)
      • 22. Asgari, M., Shafran, I.: ‘Improvements to harmonic model for extracting better speech features in clinical applications’, Comput. Speech. Lang., 2018, 47, pp. 298313.
    23. 23)
      • 23. Forney, G.D.: ‘The Viterbi algorithm’, Proc. IEEE, 1973, 61, (3), pp. 268278.
    24. 24)
      • 24. Mooney, C.Z.: ‘Monte Carlo simulation’ (Sage Publications, Thousand Oaks, CA,USA, 1997), Vol. 116.
    25. 25)
      • 25. MIT-BIH Database: Available athttps://www.physionet.org/cgi-bin/atm/ATM.
    26. 26)
      • 26. Shahriari, Y., Fidler, R., Pelter, M., et al: ‘Electrocardiogram signal quality assessment based on structural image similarity metric’, IEEE Trans. Biomed. Eng., 2017, PP, 65, (99), pp. 19.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2018.5162
Loading

Related content

content/journals/10.1049/iet-spr.2018.5162
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address