An Improved Wavelet Threshold Algorithm for ECG Denoising_Journal of Biomedical Engineering

Authors：

 LIUXiuling ¹ , QIAOLei ¹ , YANGJianli ¹ , DONGBin ² , WANGHongrui ¹

1. Biomedical Engineering Research Center, College of Electronics and Information Engineering, Hebei University, Baoding 071000, China;
2. Affiliated Hospital of Hebei University, Baoding 071000, China;

Corresponding author：

LIUXiuling, Email: liuxiuling121@hotmail.com

Keywords：

electrocardiogram signal; denoising; wavelet transform; subband; threshold

DOI：

10.7507/1001-5515.20140095

Video：

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Abstract Full text Figures/Tables Video References Cited by

Due to the characteristics and environmental factors, electrocardiogram (ECG) signals are usually interfered by noises in the course of signal acquisition, so it is crucial for ECG intelligent analysis to eliminate noises in ECG signals. On the basis of wavelet transform, threshold parameters were improved and a more appropriate threshold expression was proposed. The discrete wavelet coefficients were processed using the improved threshold parameters, the accurate wavelet coefficients without noises were gained through inverse discrete wavelet transform, and then more original signal coefficients could be preserved. MIT-BIH arrythmia database was used to validate the method. Simulation results showed that the improved method could achieve better denoising effect than the traditional ones.

Citation： LIUXiuling, QIAOLei, YANGJianli, DONGBin, WANGHongrui. An Improved Wavelet Threshold Algorithm for ECG Denoising. Journal of Biomedical Engineering, 2014, 31(3): 511-515. doi: 10.7507/1001-5515.20140095 Copy

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13.	CLIFFORD G, TARASSENKO L, TOWNSEND N. One-pass training of optimal architecture auto-associative neural network for detecting ectopic beats[J]. Electron Lett,2001.37(18):1126-1127.
14.	SAMENI R, SHAMSOLLAHI M B, JUTTEN C, et al. A nonlinear Bayesian filtering framework for ECG denoising[J]. IEEE Trans Biomed Eng, 2007, 54(12):2172-2185.
15.	DONOHO D L, JOHNSTONE I M, KERKYACHARIAN G, et al. Wavelet shrinkage:Asymptopia?[J]. J R Statist Soc B, 1995, 57(2):301-369.
16.	POORNACHANDRA S. Wavelet-based denoising using subband dependent threshold for ECG signals[J]. Digit Signal Process, 2008, 18(1):49-55.
17.	DONOHO D L, JOHNSTONE I M. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika, 1994, 81(3):425-455.
18.	DONOHO D L, ELAD M, TEMLYAKOV V. Stable recovery of sparse overcomplete representations in the presence of noise[J]. IEEE Trans Inf Theory, 2006, 52(1):6-18.
19.	DONOHO D L, JOHNSTONE I M. Adapting to unknown smoothness via wavelet shrinkage[J]. J Am Statist Assoc, 1995, 90(432):1200-1224.
20.	DONOHO D L. Compressed sensing[J]. IEEE Trans Inf Theory. 2006.52(4):1289-1306.
21.	DONOHO D L. De-noising by soft-thresholding[J]. IEEE Trans Inf Theor, 1995, 41(3):613-627.
22.	高彩红,李天博,钱坤喜,等.基于MATLAB的心电去噪中小波基选取研究[J].微计算机信息,2010,26(7-1):222-223, 221.
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24.	许原.心电图解读[J].中国临床医生,2004,32(4):18-19.

1. BLANCO-VELASCO M, WENG B, BARNER K E. A new ECG enhancement algorithm for stress ECG tests^[C]//Computers in Cardiology. Valencia:2006:917-920.
2. YAN H, LI Y J. Electrocardiogram analysis based on the Karhunen-Loève transorm[C]//Proceedings of the 3rd International Conference on Biomedical Engineering and Informatics. Yantai:2010:887-890.
3. VETTERLI M. Wavelets, approximation, and compression[J]. IEEE Signal Process Mag, 2001, 18(5):59-73.
4. POPESCU M, CRISTEA P, BEZERIANOS A. High resolution ECG filtering using adaptive Bayesian wavelet shrinkage[C]//Computers in Cardiology. Cleveland, OH, USA:1998:401-404.
5. ALMENAR V, ALBIOL A. A new adaptive scheme for ECG enhancement[J]. Signal Process., 1999, 75(9):253-263.
6. 万相奎,张军.基于小波神经网络的心电信号滤波研究[J].生物医学工程学杂志,2010,27(6):1197-1201.
7. 张翔,周严.基于自适应滤波的心电图中呼吸干扰的抑制[J].生物医学工程学杂志,2011,28(1):58-62.
8. ALFAOURI M, DAQROUQ K. ECG signal denoising by wavelet transform thresholding[J]. Am J Appl Sci, 2008, 5(3):276-281.
9. SAYADI O, SHAMSOLLAHI M B. ECG denoising with adaptive bionic wavelet transform[C]//Proceedings of 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. New York, NY:2006:6597-6600.
10. BARROS A K, MANSOUR A, OHNISHI N. Removing artifacts from electrocardiographic signals using independent components analysis[J]. Neurocomputing, 1998, 22(1-3):173-186.
11. HE T G, CLIFFORD G, TARASSENKO L. Application of independent component analysis in removing artefacts from the electrocardiogram[J]. Neural Comput Appl, 2006, 15(2):105-116.
12. CHAWLA M P S, VERMA H K, KUMAR V. ECG modeling and QRS detection using principal component analysis[C]//Proceedings of the IET 3rd International Conference on Advances in Medical, Signal and Information Processing. Glasgow, UK:2006:1-4.
13. CLIFFORD G, TARASSENKO L, TOWNSEND N. One-pass training of optimal architecture auto-associative neural network for detecting ectopic beats[J]. Electron Lett,2001.37(18):1126-1127.
14. SAMENI R, SHAMSOLLAHI M B, JUTTEN C, et al. A nonlinear Bayesian filtering framework for ECG denoising[J]. IEEE Trans Biomed Eng, 2007, 54(12):2172-2185.
15. DONOHO D L, JOHNSTONE I M, KERKYACHARIAN G, et al. Wavelet shrinkage:Asymptopia?[J]. J R Statist Soc B, 1995, 57(2):301-369.
16. POORNACHANDRA S. Wavelet-based denoising using subband dependent threshold for ECG signals[J]. Digit Signal Process, 2008, 18(1):49-55.
17. DONOHO D L, JOHNSTONE I M. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika, 1994, 81(3):425-455.
18. DONOHO D L, ELAD M, TEMLYAKOV V. Stable recovery of sparse overcomplete representations in the presence of noise[J]. IEEE Trans Inf Theory, 2006, 52(1):6-18.
19. DONOHO D L, JOHNSTONE I M. Adapting to unknown smoothness via wavelet shrinkage[J]. J Am Statist Assoc, 1995, 90(432):1200-1224.
20. DONOHO D L. Compressed sensing[J]. IEEE Trans Inf Theory. 2006.52(4):1289-1306.
21. DONOHO D L. De-noising by soft-thresholding[J]. IEEE Trans Inf Theor, 1995, 41(3):613-627.
22. 高彩红,李天博,钱坤喜,等.基于MATLAB的心电去噪中小波基选取研究[J].微计算机信息,2010,26(7-1):222-223, 221.
23. 何俊,马有良.心电信号降噪小波函数选取的定量研究[J].信息与电子工程,2010,08(3):286-289.
24. 许原.心电图解读[J].中国临床医生,2004,32(4):18-19.

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