Robust Approach of De-noising ECG Signal Using Multi-Resolution Wavelet Transform
-
2018-10-02 https://doi.org/10.14419/ijet.v7i4.11.20678
-
ECG signal, Noise Reduction, Symlet Filter, Discrete Wavelet Transform, MIT-BIH. -
Abstract
The ECG signal expresses the behavior of human heart against time. The analysis of this signal performs great information for diagnosing different cardiac diseases. In other hand, the ECG signal used for analyzing must be clean from any type of noises that corrupted it by the external environment. In this paper, a new approach of ECG signal noise reduction is proposed to minimize noise from all parts of ECG signal and maintains main characteristics of ECG signal with lowest changes. The new approach applies simple scaling down operation on the detail resolution in the wavelet transform space of noisy signal. The proposed noise reduction approach is validated by some ECG records from MIT-BIH database. Also, the performance of the proposed approach is evaluated graphically using different SNR levels and some standard metrics. The results improve the ability of the proposed approach to reduce noise from the ECG signal with high accuracy in comparison to the existing methods of noise reduction.
Â
Â
-
References
[1] A. Gacek, ECG Signal processing, classification and interpretation: A comprehensive framework of computational intelligence. Springer Science and Business Media, 2011.
[2] J. S. Suri and J. A. Spaan, Advances in cardiac signal processing. Springer, 2007.
[3] O. Sayadi and M. B. Shamsollahi, "ECG denoising with adaptive bionic wavelet transform," Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006, pp. 6597-6600.
[4] J. R. Hampton, The ECG made easy e-book. Elsevier Health Sciences, 2008.
[5] P. Malmivuo, J. Malmivuo and R. Plonsey, Bioelectromagnetism: Principles and applications of bioelectric and biomagnetic fields. Oxford University Press, 1995.
[6] E. N. Bruce, Biomedical signal processing and signal modeling. Wiley, 2001.
[7] R. M. Rangayyan, Biomedical signal analysis. John Wiley and Sons, 2015.
[8] S. Poungponsri and X.-H. Yu, "An Adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks," Neurocomputing, 117, 206-213, 2013.
[9] M. A. Kabir and C. Shahnaz, "Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains," Biomedical Signal Processing and Control, 7, 481-489, 2012.
[10] M. Suchetha and N. Kumaravel, "Empirical mode decomposition based filtering techniques for power line interference reduction in electrocardiogram using various adaptive structures and subtraction methods," Biomedical Signal Processing and Control, 8, 575-585, 2013.
[11] M. Z. U. Rahman, R. A. Shaik, and D. Rama Koti Reddy, "Efficient sign based normalized adaptive filtering techniques for cancelation of artifacts in ECG signals: Application to wireless biotelemetry," Signal Processing, 91, 225-239, 2011.
[12] G. B. Moody and R. G. Mark, "The impact of the MIT-BIH arrhythmia database," IEEE Engineering in Medicine and Biology Magazine, 20, 45-50, 2001.
[13] A. Elbuni, S. Kanoun, M. Elbuni, and N. Ali, "ECG parameter extraction algorithm using (DWTAE) algorithm," Proceedings of the International Conference on Computer Engineering and Systems, 2009, pp. 315-320.
[14] M. Alfaouri and K. Daqrouq, "ECG signal denoising by wavelet transform thresholding," American Journal of Applied Sciences, 5, 276, 2008.
[15] N. Li and P. Li, "An improved algorithm based on EMD-wavelet for ECG signal de-noising," Proceedings of the International Joint Conference on Computational Sciences and Optimization, 2009, pp. 825-827.
[16] H. Kasturiwale and C. Deshmukh, "Quality assessment of ICA for ECG signal analysis," Proceedings of the 2nd International Conference on Emerging Trends in Engineering and Technology, 2009, pp. 73-75.
[17] G. Tang and A. Qin, "ECG de-noising based on empirical mode decomposition," Proceedings of the 9th International Conference for Young Computer Scientists, 2008, pp. 903-906.
-
Downloads
-
How to Cite
Jaenul, A., Alyousif, S., Amer Ahmed Alrawi, A., & K. Salih, S. (2018). Robust Approach of De-noising ECG Signal Using Multi-Resolution Wavelet Transform. International Journal of Engineering & Technology, 7(4.11), 5-8. https://doi.org/10.14419/ijet.v7i4.11.20678Received date: 2018-09-30
Accepted date: 2018-09-30
Published date: 2018-10-02