Denoising Ambulatory Electrocardiogram Signal Using Interval Dedependent Thresholds based Stationary Wavelet Transform
DOI: http://dx.doi.org/10.62527/joiv.8.2.2428
Abstract
Noise contamination in electrocardiogram (ECG) monitoring systems can lead to errors in analysis and diagnosis, resulting in a high false alarm rate (FAR). Various studies have been conducted to reduce or eliminate noise in ECG signals. However, some noise characteristics overlap with the frequency range of ECG signals, which occur randomly and are transient. This results in shape alteration and amplitude reduction in P and R waves. The author proposed a framework for eliminating noise in ECG signals using the stationary wavelet transform method and interval-dependent thresholds (IDT) based on the change point detection method to address these challenges. The proposed framework decomposes the input electrocardiogram (ECG) signal at a specific level using the Stationary Wavelet Transform method, resulting in detail and approximation coefficients. Interval detection focuses on the initial detailed coefficient, d1, chosen due to its significant content of noise coefficients, especially high-frequency noise. Subsequently, threshold values are computed for each interval. Hard and soft thresholding processes are then applied individually to each interval. Finally, reconstruction occurs using the inverse stationary wavelet transform method on the threshold coefficient outcomes. Two measurement matrices, root mean square error (RMSE) and percentage root mean squared difference (PRD), were used to measure the performance of the proposed framework. In addition, the proposed framework was compared to stationary wavelet transform (SWT) and discrete wavelet transform (DWT). The test results showed that the proposed method outperforms DWT and SWT. The proposed framework obtained an average increase in RMSE scores of 18% and 45% compared to the SWT and DWT methods, respectively, and PRD values of 17% and 37% compared to the SWT and DWT methods, respectively. So, using IDT in the stationary wavelet transform method can improve the denoising performance. With the development of this new framework for denoising ECG signals, we hope it can become an alternative method for other researchers to utilize in denoising ECG signals.
Keywords
References
WHO, Noncommunicable Diseases Country Profiles 2018, vol. 369, no. 14. 2018. doi: 10.1056/NEJMra1109345.
M. Frank, Matthew G. annis, Watkins, “Frequency Content and Characteristics of Ventricular Conduction,” Physiol Behav, vol. 48, no. 80, pp. 678–687, 2019, doi: 10.1016/j.jelectrocard.2015.08.034.Frequency.
C. F. a Davis, ECG Success: Exercises in ECG Interpretation. 2008. doi: 0803613636.
A. Kumar, H. Tomar, V. K. Mehla, R. Komaragiri, and M. Kumar, “Stationary wavelet transform based ECG signal denoising method,” ISA Trans, vol. 114, pp. 251–262, Aug. 2021, doi: 10.1016/j.isatra.2020.12.029.
L. M. Eerikäinen, J. Vanschoren, M. J. Rooijakkers, R. Vullings, and R. M. Aarts, “Decreasing the false alarm rate of arrhythmias in intensive care using a machine learning approach,” Comput Cardiol (2010), vol. 42, pp. 293–296, 2015, doi: 10.1109/CIC.2015.7408644.
J. Oster, J. Behar, O. Sayadi, S. Nemati, A. E. W. Johnson, and G. D. Clifford, “Semisupervised ECG Ventricular Beat Classification with Novelty Detection Based on Switching Kalman Filters,” IEEE Trans Biomed Eng, vol. 62, no. 9, pp. 2125–2134, 2015, doi: 10.1109/TBME.2015.2402236.
U. Satija, B. Ramkumar, and M. S. Manikandan, “A unified sparse signal decomposition and reconstruction framework for elimination of muscle artifacts from ECG signal,” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2016-May, pp. 779–783, 2016, doi: 10.1109/ICASSP.2016.7471781.
J. Moeyersons et al., “Artefact detection and quality assessment of ambulatory ECG signals,” Comput Methods Programs Biomed, vol. 182, Dec. 2019, doi: 10.1016/j.cmpb.2019.105050.
Z. Wang, F. Wan, C. M. Wong, and L. Zhang, “Adaptive Fourier decomposition based ECG denoising,” Comput Biol Med, vol. 77, pp. 195–205, 2016, doi: 10.1016/j.compbiomed.2016.08.013.
Z. Wang, C. M. Wong, and F. Wan, “Adaptive Fourier decomposition based R-peak detection for noisy ECG Signals,” Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 3501–3504, 2017, doi: 10.1109/EMBC.2017.8037611.
Z. Wang et al., “Muscle and electrode motion artifacts reduction in ECG using adaptive fourier decomposition,” Conf Proc IEEE Int Conf Syst Man Cybern, vol. 2014-Janua, no. January, pp. 1456–1461, 2014, doi: 10.1109/smc.2014.6974120.
S. Abbaspour, H. Gholamhosseini, and M. Linden, “Evaluation of wavelet based methods in removing motion artifact from ECG signal,” IFMBE Proc, vol. 48, pp. 1–4, 2015, doi: 10.1007/978-3-319-12967-9_1.
G. Han, B. Lin, and Z. Xu, “Electrocardiogram signal denoising based on empirical mode decomposition technique: An overview,” Journal of Instrumentation, vol. 12, no. 3, 2017, doi: 10.1088/1748-0221/12/03/P03010.
S. Jain, V. Bajaj, and A. Kumar, “Riemann Liouvelle Fractional Integral based Empirical Mode Decomposition for ECG Denoising,” IEEE J Biomed Health Inform, vol. 22, no. c, pp. 1133–1139, 2017, doi: 10.1109/JBHI.2017.2753321.
L. E. L. Bouny, M. Khalil, A. Adib, and L. I. M. Ii-fstm, “ECG Signal Denoising Based on Ensemble EMD Thresholding and Higher Order Statistics,” pp. 1–6, 2017.
M. Rakshit, “An Improved EMD based ECG Denoising Method using Adaptive Switching Mean Filter,” pp. 4–8, 2017.
M. Rakshit and S. Das, “An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter,” Biomed Signal Process Control, vol. 40, pp. 140–148, Feb. 2018, doi: 10.1016/j.bspc.2017.09.020.
S. Nagai, D. Anzai, and J. Wang, “Motion artefact removals for wearable ECG using stationary wavelet transform,” Healthc Technol Lett, vol. 4, no. 4, pp. 138–141, 2017, doi: 10.1049/htl.2016.0100.
S. Nagai, D. Anzai, and J. Wang, “Motion artifact removal for wearable ECG using stationary wavelet multi-resolution analysis,” IEEE International Symposium on Electromagnetic Compatibility, vol. 2017-Octob, no. 1, pp. 1–5, 2018, doi: 10.1109/EMC-B.2017.8260359.
L. El Bouny, M. Khalil, and A. Adib, “Performance analysis of ECG signal denoising methods in transform domain,” 2018 International Conference on Intelligent Systems and Computer Vision, ISCV 2018, vol. 2018-May, pp. 1–8, 2018, doi: 10.1109/ISACV.2018.8354038.
M. Sraitih and Y. Jabrane, “A denoising performance comparison based on ECG Signal Decomposition and local means filtering,” Biomed Signal Process Control, vol. 69, Aug. 2021, doi: 10.1016/j.bspc.2021.102903.
U. Satija, B. Ramkumar, and M. S. Manikandan, “A robust sparse signal decomposition framework for baseline wander removal from ECG signal,” IEEE Region 10 Annual International Conference, Proceedings/TENCON, no. 2, pp. 2470–2473, 2017, doi: 10.1109/TENCON.2016.7848477.
D. Berwal, C. R. Vandana, S. Dewan, C. V. Jiji, and M. S. Baghini, “Motion Artifact Removal in Ambulatory ECG Signal for Heart Rate Variability Analysis,” IEEE Sens J, vol. 19, no. 24, pp. 12432–12442, Dec. 2019, doi: 10.1109/JSEN.2019.2939391.
Y. H. Peng, “De-noising by modified soft-thresholding,” IEEE Asia-Pacific Conference on Circuits and Systems - Proceedings, vol. 41, no. 3, pp. 760–762, 2000, doi: 10.1109/apccas.2000.913631.
D. L. Donoho and J. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika, vol. 81, no. 3, pp. 425–455, 1994, doi: 10.1093/biomet/81.3.425.
R. Kumar and P. Patel, “Signal Denoising with Interval Dependent Thresholding Using DWT and SWT,” International Journal of Innovative Technology and Exploring Engineering, vol. 1, no. 6, pp. 47–50, 2012.
Q. Zhang, R. Aliaga-Rossel, and P. Choi, “Denoising of gamma-ray signals by interval-dependent thresholds of wavelet analysis,” Meas Sci Technol, vol. 17, no. 4, pp. 731–735, 2006, doi: 10.1088/0957-0233/17/4/019.
J. Velandy and J. Surendran, “Interval dependent wavelet de-noising technique for high frequency transient signals analysis during impulse testing of transformers,” 9th International Conference on Industrial and Information Systems, ICIIS 2014, pp. 1–4, 2015, doi: 10.1109/ICIINFS.2014.7036605.
L. Hristov, E. Iontchev, R. Miletiev, and P. Kapanakov, “Wavelet algorithm for denoising MEMS sensor data,” no. June, pp. 27–29, 2019.
C. Beale, C. Niezrecki, and M. Inalpolat, “An adaptive wavelet packet denoising algorithm for enhanced active acoustic damage detection from wind turbine blades,” Mech Syst Signal Process, vol. 142, 2020, doi: 10.1016/j.ymssp.2020.106754.
J.-P. Antoine, “Wavelet Transforms and Their ApplicationsWavelet Transforms and Their Applications , Lokenath Debnath , Birkhäuser, Boston, 2002. $79.95 (565 pp.). ISBN 0-8176-4204-8 ,” Phys Today, vol. 56, no. 4, pp. 68–68, 2007, doi: 10.1063/1.1580056.
M. Lavielle, “Detection of multiple changes in a sequence of dependent variables,” 1999. [Online]. Available: www.elsevier.com/locate/spa
M. Lavielle, “Using penalized contrasts for the change-point problem,” 2004. [Online]. Available: https://inria.hal.science/inria-00070662
R. Cohen, “Signal Denoising Using Wavelets,” 2012. [Online]. Available: http://tx.technion.ac.il/˜rc
X. Wang and Y. Dai, “An Improved Denoising Method Based on Stationary Wavelet Transform,” 2018.
G. B. Moody and R. G. Mark, “The impact of the MIT-BIH arrhythmia database.,” IEEE Eng Med Biol Mag, vol. 20, no. 3, pp. 45–50, Accessed: Oct. 28, 2018. [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/11446209
M. RG. Moody GB, Muldrow WE, “A noise stress test for arrhythmia detectors,” Computers in Cardiology , vol. 11, pp. 381–384, 1984, Accessed: Jun. 29, 2019. [Online]. Available: https://physionet.org/physiobank/database/nstdb/?C=D;O=D