Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2013, Special Issue (2013), Article ID 763903, 13 pages.

Noise Suppression in ECG Signals through Efficient One-Step Wavelet Processing Techniques

E. Castillo, D. P. Morales, A. García, F. Martínez-Martí, L. Parrilla, and A. J. Palma

Full-text: Open access

Abstract

This paper illustrates the application of the discrete wavelet transform (DWT) for wandering and noise suppression in electrocardiographic (ECG) signals. A novel one-step implementation is presented, which allows improving the overall denoising process. In addition an exhaustive study is carried out, defining threshold limits and thresholding rules for optimal wavelet denoising using this presented technique. The system has been tested using synthetic ECG signals, which allow accurately measuring the effect of the proposed processing. Moreover, results from real abdominal ECG signals acquired from pregnant women are presented in order to validate the presented approach.

Article information

Source
J. Appl. Math., Volume 2013, Special Issue (2013), Article ID 763903, 13 pages.

Dates
First available in Project Euclid: 14 March 2014

Permanent link to this document
https://projecteuclid.org/euclid.jam/1394807817

Digital Object Identifier
doi:10.1155/2013/763903

Mathematical Reviews number (MathSciNet)
MR3066318

Zentralblatt MATH identifier
1271.65160

Citation

Castillo, E.; Morales, D. P.; García, A.; Martínez-Martí, F.; Parrilla, L.; Palma, A. J. Noise Suppression in ECG Signals through Efficient One-Step Wavelet Processing Techniques. J. Appl. Math. 2013, Special Issue (2013), Article ID 763903, 13 pages. doi:10.1155/2013/763903. https://projecteuclid.org/euclid.jam/1394807817


Export citation

References

  • J. Lee, Y. Chee, and I. Kim, “Personal identification based on vectorcardiogram derived from limb leads electrocardiogram,” Journal of Applied Mathematics, vol. 2012, Article ID 904905, 12 pages, 2012.
  • D. P. Morales, A. García, E. Castillo, M. A. Carvajal, J. Banqueri, and A. J. Palma, “Flexible ECG acquisition system based on analog and digital reconfigurable devices,” Sensors and Actuators A, vol. 165, no. 2, pp. 261–270, 2011.
  • D. P. Morales, A. García, E. Castillo, M. A. Carvajal, L. Parrilla, and A. J. Palma, “An application of reconfigurable technologies for non-invasive fetal heart rate extraction,” Medical Engineering and Physics, 2013.
  • S. G. Mallat, “Theory for multiresolution signal decomposition: the wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674–693, 1989.
  • E.-B. Lin and P. C. Liu, “A discrete wavelet analysis of freak waves in the ocean,” Journal of Applied Mathematics, vol. 2004, no. 5, pp. 379–394, 2004.
  • T. Abualrub, I. Sadek, and M. Abukhaled, “Optimal control systems by time-dependent coefficients using cas wavelets,” Journal of Applied Mathematics, vol. 2009, Article ID 636271, 10 pages, 2009.
  • R. Sameni and G. D. Clifford, “A review of fetal ECG signal processing, issues and promising directions,” The Open Pacing, Electrophysiology & Therapy Journal, vol. 3, no. 1, pp. 4–20, 2010.
  • C.-T. Ku, K.-C. Hung, H.-S. Wang, and Y.-S. Hung, “High efficient ECG compression based on reversible round-off non-recursive 1-D discrete periodized wavelet transform,” Medical Engineering and Physics, vol. 29, no. 10, pp. 1149–1166, 2007.
  • C. Ghule, D. G. Wakde, G. Virdi, and N. R. Khodke, “Design of portable ARM processor based ECG module for 12 lead ECG data acquisition and analysis,” in Proceedings of the 2nd International Conference on Biomedical and Pharmaceutical Engineering (ICBPE '09), pp. 1–8, December 2009.
  • D. Karadaglić, M. Mirković, D. Milošević, and R. Stojanović, “A FPGA system for QRS complex detection based on Integer Wavelet Transform,” Measurement Science Review, vol. 11, no. 4, pp. 131–138, 2011.
  • J. C. Goswami and A. K. Chan, Fundamentals of Wavelets Theory, Algorithms, and Applications, EE.UU: John Wiley & Sons, Hoboken, NJ, USA, 2nd edition, 2011.
  • P. S. Addison, “Wavelet transforms and the ECG: a review,” Physiological Measurement, vol. 26, no. 5, pp. R155–R199, 2005.
  • B. N. Singh and A. K. Tiwari, “Optimal selection of wavelet basis function applied to ECG signal denoising,” Digital Signal Processing, vol. 16, no. 3, pp. 275–287, 2006.
  • C. B. Mbachu, G. N. Onoh, E. N. Ifeagwu, and S. U. Nnebe, “Processing ECG signal with Kaiser Window-Based FIR digital dilters,” International Journal of Engineering Science and Technology, vol. 3, no. 8, 2011.
  • L. N. Sharma, S. Dandapat, and A. Mahanta, “ECG signal denoising using higher order statistics in Wavelet subbands,” Biomedical Signal Processing and Control, vol. 5, no. 3, pp. 214–222, 2010.
  • M. I. Ibrahimy, F. Ahmed, M. A. Mohd Ali, and E. Zahedi, “Real-time signal processing for fetal heart rate monitoring,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 2, pp. 258–262, 2003.
  • J. J. Oresko, Z. Jin, J. Cheng et al., “A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing,” IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 3, pp. 734–740, 2010.
  • B. De Moor, Database for the identification of systems (DaISy), 2010, http://homes.esat.kuleuven.be/$\sim\,\!$smc/daisy/.
  • J. G. Webster, Medical Instrumentation, Application and Design, John Wiley & Sons, 1995.
  • M. S. Manikandan and S. Dandapat, “Wavelet energy based diagnostic distortion measure for ECG,” Biomedical Signal Processing and Control, vol. 2, no. 2, pp. 80–96, 2007.
  • D. Donoho and I. Johnstone, “Adapting to unknown smoothness via wavelet shrinkage,” Journal of the American Statistical Association, vol. 90, no. 432, pp. 1200–1224, 1995.
  • Inc. The MathWorks, Denoising: Wavelet shrinkage, nonparametric regression, block thresholding, multisignal threshold-ing, 2013, http://www.mathworks.es/es/help/wavelet/denois- ing.html.
  • LabView TM, Advanced Signal Processing Toolkit, Wavelet Analysis Tools User Manual, 2013, http://www.ni.com/pdf/ manuals/371533a.pdf.
  • S. Sardy, “Minimax threshold for denoising complex signals with waveshrink,” IEEE Transactions on Signal Processing, vol. 48, no. 4, pp. 1023–1028, 2000.
  • P. J. Rousseeuw and C. Croux, “Alternatives to the median absolute deviation,” Journal of the American Statistical Association, vol. 88, no. 424, pp. 1273–1283, 1993.
  • I. M. Johnstone and B. W. Silverman, “Wavelet threshold estimators for data with correlated noise,” Journal of the Royal Statistical Society B, vol. 59, no. 2, pp. 319–351, 1997.
  • W. H. Swallow and F. Kianifard, “Using robust scale estimates in detecting multiple outliers in linear regression,” Biometrics, vol. 52, no. 2, pp. 545–556, 1996.
  • Matlab, ECG simulation Using atlab, 2013, http://www.math- works.com/matlabcentral/fileexchange/10858.
  • H. G. Rodney Tan, A. C. Tan, P. Y. Khong, and V. H. Mok, “Best wavelet function identification system for ECG signal denoise applications,” in Proceedings of the International Conference on Intelligent and Advanced Systems (ICIAS '07), pp. 631–634, November 2007.
  • A. L. Goldberger, L. A. Amaral, L. Glass et al., “PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. E215–E220, 2000.
  • J. Jezewski, A. Matonia, T. Kupka, D. Roj, and R. Czabanski, “Determination of fetal heart rate from abdominal signals: evaluation of beat-to-beat accuracy in relation to the direct fetal electrocardiogram,” Biomedical Engineering, vol. 57, no. 5, pp. 383–394, 2012.