An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks

Neurocomputing

Volumn 117, Issue , 2013, Pages 206-213

  Poungponsri, Suranai ^a   Yu, Xiao Hua ^a  

^a CALIFORNIA POLYTECHNIC STATE UNIVERSITY   (United States)

Author keywords

ECG signal noise reduction; Neural networks; Wavelet transform

Indexed keywords

ADAPTIVE LEARNING; CONVENTIONAL METHODS; ECG SIGNAL PROCESSING; ECG SIGNALS; ELECTROCARDIOGRAM SIGNAL; MULTIRESOLUTION PROPERTY; MUSCLE CONTRACTIONS; POWERLINE INTERFERENCE;

ADAPTIVE FILTERING; COMPUTER SIMULATION; DISCRETE WAVELET TRANSFORMS; NEURAL NETWORKS; SIGNAL NOISE MEASUREMENT; SIGNAL PROCESSING; SIGNAL TO NOISE RATIO; WAVELET DECOMPOSITION; WAVELET TRANSFORMS;

ELECTROCARDIOGRAPHY;

ARTICLE; ARTIFACT REDUCTION; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; CONTROLLED STUDY; ELECTROCARDIOGRAM; INTERMETHOD COMPARISON; NOISE REDUCTION; PRIORITY JOURNAL; SIGNAL NOISE RATIO; SIGNAL PROCESSING; WAVELET ANALYSIS; WAVELET TRANSFORM;

EID: 84878924644     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.02.010     Document Type: Article

References (29)

1. Sinus Rhythm. Accessed January 2012. http://www.en.wikipedia.org/wiki/Sinus_rhythm.
2. Friesen G., Jannett T., Jadallah M., Yates S., Quint S., Nagle H. A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Trans. Biomed. Eng. 1990, 37(1):85-98.
3. Kannathal N., Acharya U., Joseph P., Min L., Suri J. Analysis of electrocardiograms. Advances in Cardiac Signal Processing 2007, 55-82. Springer. U. Acharya, J. Suri, J. Spaan, S. Krishnan (Eds.).
4. Slonim T., Slonim M., Ovsyscher E. The use of simple FIR filters for filtering of ECG signals and a new method for post-filter signal reconstruction. Proc. Comput. Cardiol. 1993, 871-873.
5. V. Afonso, W. Tompkins, T. Nguyen, S. Trautmann, S. Luo, Filter bank-based processing of the stress ECG, in: Proceedings of IEEE 17th Annual Conference on Engineering in Medicine and Biology Society, 1995, pp. 887-888.
6. Wu Y., Rangayyan R., Zhou Y., Ng S. Filtering electrocardiographic signals using an unbiased and normalized adaptive noise reduction system. Med. Eng. Phys. 2009, 31:17-26.
7. Sayadi O., Shamsollahi M. ECG denoising and compression using a modified extended Kalman filter structure. IEEE Trans. Biomed. Eng. 2008, 55(9):2240-2248.
8. Yan J., Lu Y., Liu J., Wu X., Xu Y. Self-adaptive model-based ECG denoising using features extracted by mean shift algorithm. Biomed. Signal Process. Control 2010, 5:103-113.
9. Blanco-Velascoa M., Weng B., Barner K. ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Comput. Biol. Med. 2008, 38:1-13.
10. Poornachandra S. Wavelet-based denoising using subband dependent threshold for ECG signals. Digital Signal Process. 2008, 18:49-55.
11. Alfaouri M., Daqrouq K. ECG signal denoising by wavelet transform thresholding. Am. J. Appl. Sci. 2008, 5(3):276-281.
12. O. Sayadi, M. Shamsollahi, Multiadaptive bionic wavelet transform: application to ECG denoising and baseline wandering reduction, EURASIP J. Adv. Signal Process. (2007). http://dx.doi.org/10.1155/2007/41274.
13. Jiang W., Kong S. Block-based neural networks for personalized ECG signal classification. IEEE Trans. Neural Networks 2007, 18(6):1750-1761.
14. S. Jadhav, S. Nalbalwar, A. Ghatol, Artificial neural network based cardiac arrhythmia classification using ECG signal data, in: Proceedings of the International Conference on Electronics and Information Engineering, 2010, pp. V1-228-V1-231.
15. Dey N., Dash T., Dash S. ECG signal denoising by functional link artificial neural network (FLANN). Int. J. Biomed. Eng. Technol. 2011, 7(4):377-389.
16. Zhang Q., Benveniste A. Wavelet networks. IEEE Trans. Neural Networks 1992, 3(6):889-898.
17. S. Poungponsri, X.-H. Yu, Electrocardiogram (ECG) signal modeling and noise reduction using wavelet neural networks, in: Proceedings of the International Conference on Automation and Logistics, 2009, pp. 394-398.
18. S. Mahmoodabadi, A. Ahmadian, M. Abolhasani, ECG feature extraction using Daubechies wavelets, in: Proceedings of the IASTED International Conference on Visualization, Imaging, and Image Processing, 2005, pp. 343-348.
19. Haykin S. Neural Networks and Learning Machines 2008, Prentice-Hall. third ed.
20. Moody G., Mark R. The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 2001, 20(3):45-50.
21. Moody G., Muldrow W., Mark R., Noise Stress A. Test for arrhythmia detectors. Proc. Comput. Cardiol. 1984, 381-384.
22. Accessed January 2012. http://www.en.wikipedia.org/wiki/Respiratory_rate.
23. G. Moody, WFDB Applications Guide, Accessed January 2012. http://www.physionet.org/physiotools/wag/.
24. Liang H., Lin Z., Yin F. Removal of ECG contamination from diaphragmatic EMG by nonlinear analysis. Nonlinear Anal. 2005, 63:745-753.
25. Liang H., Lin Z. Stimulus artifact cancellation in gastric myoelectrical recordings using wavelet transform. IEEE Trans. Biomed. Eng. 2002, 49(7):681-688.
26. Wang Z., Maier A., Leopold D., Logothetis N., Liang H. Single-trial evoked potential estimation using wavelet. Comput. Biol. Med. 2007, 37:46-473.
27. Liang H., Lin Q., Chen J. Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease. IEEE Trans. Biomed. Eng. 2005, 52(10):1692-1701.
28. Accessed December 2012. http://www.en.wikipedia.org/wiki/Discrete_wavelet_transform.
29. H. Zang, Z. Wang, Y. Zheng, Analysis of signal de-noising method based on an improved wavelet thresholding, in: The Ninth International Conference on Electronic Measurement & Instruments, 2009, pp. I-987-I-990.