An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter
Source abstract
Abstract
Electrocardiographic (ECG) signal is essential to diagnose and analyse cardiac disease. However, ECG signals are susceptible to be contaminated with various noises, which affect the application value of ECG signals. In this paper, we propose an ECG signal de-noising method using wavelet energy and a sub-band smoothing filter. Unlike the traditional wavelet threshold de-noising method, which carries out threshold processing for all wavelet coefficients, the wavelet coefficients that require threshold de-noising are selected according to the wavelet energy and other wavelet coefficients remain unchanged in the proposed method. Moreover, The sub-band smoothing filter is adopted to further de-noise the ECG signal and improve the ECG signal quality. The ECG signals of the standard MIT-BIH database are adopted to verify the proposed method using MATLAB software. The performance of the proposed approach is assessed using Signal-To-Noise ratio (SNR), Mean Square Error (MSE) and percent root mean square difference (PRD). The experimental results illustrate that the proposed method can effectively remove noise from the noisy ECG signals in comparison to the existing methods.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: ECG Monitoring and Analysis · Image and Signal Denoising Methods · Non-Invasive Vital Sign Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Computer and Communication Engineering Institute Changsha University of Science and Technology, Changsha 410004, China
Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science and Technology, Changsha 410114, China
School of Computing Science and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, India
Department of Computer Science, Texas Tech University, 2500 Broadway, Lubbock, TX 79409, USA
School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 425100, China
Funding
National Natural Science Foundation of China · "Double First-class" International Cooperation and Development Scientific 234 Research Project of Changsha University of Science and Technology · Young Teacher 235 Growth Plan Project of Changsha University of Science and Technology
References (48)
Show source-provided references
1. Scheidt, S., and Netter, F.H. (1986). Basic Electrocardiography, CIBA-GEIGY Pharmaceuticals.
2. An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform10.1016/j.bbe.2016.04.001 ↗
3. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis10.1098/rspa.1998.0193 ↗
4. Tang, G.T.G., and Qin, A.Q.A. (2008, January 18–21). ECG De-noising Based on Empirical Mode Decomposition. Proceedings of the 2008 The 9th International Conference for Young Computer Scientists, Hunan, China.10.1109/ICYCS.2008.178 ↗
5. ECG denoising and compression using a modified extended Kalman filter structure10.1109/TBME.2008.921150 ↗
6. A revised scheme for real time ECG Signal denoising based on recursive filtering10.1016/j.bspc.2016.02.007 ↗
7. A novel method for the elimination of power line frequency in ECG signal using hyper shrinkage function10.1016/j.dsp.2007.03.011 ↗
8. Hyper-trim shrinkage for denoising of ECG signal10.1016/j.dsp.2004.12.005 ↗
9. Singh, O., and Sunkaria, R.K. (2013, January 26–28). ECG signal denoising based on empirical mode decomposition and moving average filter. Proceedings of the 2013 IEEE International Conference on Signal Processing, Computing and Control (ISPCC), Solan, India.
10. Ideal spatial adaptation by wavelet shrinkage10.1093/biomet/81.3.425 ↗
11. De-noising by soft-thresholding10.1109/18.382009 ↗
12. ECG Signal Denoising By Wavelet Transform Thresholding10.3844/ajassp.2008.276.281 ↗
13. Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains10.1016/j.bspc.2011.11.003 ↗
14. Patil, H.T., and Holambe, R.S. (2013, January 13–15). New approach of threshold estimation for denoising ECG signal using wavelet transform. Proceedings of the 2013 Annual IEEE India Conference (INDICON), Mumbai, India.10.1109/INDCON.2013.6726038 ↗
15. Bouny, L.E., Khalil, M., and Adib, A. (2017, January 22–24). ECG signal denoising based on ensemble emd thresholding and higher order statistics. Proceedings of the 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Fez, Morocco.10.1109/ATSIP.2017.8075546 ↗
16. An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter10.1016/j.bspc.2017.09.020 ↗
17. A novel technique for muscle onset detection using surfaceemg signals without removal of ECG artifacts10.1088/0967-3334/35/1/45 ↗
18. A simple SSA-based denoising technique to remove ECG interferencein EMG signals10.1016/j.bspc.2016.06.001 ↗
19. Capacitively coupled electrocardiogram measuring system and noise reduction by singular spectrum analysis10.1109/JSEN.2016.2532599 ↗
20. An Improved SSA-Based Technique for EMG Removal from ECG10.1016/j.irbm.2018.11.004 ↗
21. Mallat, S. (1998). A Wavelet Tour of Signal Processing, Academic Press.10.1016/B978-012466606-1/50008-8 ↗
22. Redundant discrete wavelet transform for ECG signal processing
23. ECG Signal De-noising using Discrete Wavelet Transform for removal of 50 Hz PLI noise
24. (2005, February 24). The MIT-BIH Arrhythmia Database. Available online: https://www.physionet.org/content/mitdb/1.0.0/.
25. A dynamical model for generating synthetic electrocardiogram signals10.1109/TBME.2003.808805 ↗
26. McSharry, P.E., and Clifford, G.D. (2003, December 03). ECGSYN-A Realistic ECG Waveform Generator. Available online: http://www.physionet.org/physiotools/ecgsyn.
27. Quantitative investigation of digital filters in electrocardiogram with simulated noises
28. Multiadaptive bionic wavelet transform: Application to ECG denoising and baseline wandering reduction10.1155/2007/41274 ↗
29. Parallel-type fractional zero-phase filtering for ECG signal denoising10.1016/j.bspc.2014.10.012 ↗
30. Fractional zero-phase filtering based on the Riemann-Liouville integral10.1016/j.sigpro.2013.11.024 ↗
31. Random-filtering based sparse representation parallel face recognition10.1007/s11042-018-6166-3 ↗
32. Tirkolaee, E., Hosseinabadi, A., Soltani, M., Sangaiah, A., and Wang, J. (2018). A Hybrid Genetic Algorithm for Multi-trip Green Capacitated Arc Routing Problem in the Scope of Urban Services. Sustainability, 10.10.3390/su10051366 ↗
33. Single-Image Super-Resolution Algorithm Based on Structural Self-Similarity and Deformation Block Features10.1109/ACCESS.2019.2911892 ↗
34. Zhang, J., Jin, X., Sun, J., Wang, J., and Sangaiah, A.K. (2018). Spatial and semantic convolutional features for robust visual object tracking. Multimed. Tools Appl.10.1007/s11042-018-6562-8 ↗
35. Dual model learning combined with multiple feature selection for accurate visual tracking10.1109/ACCESS.2019.2908668 ↗
36. Residual domain dictionary learning for compressed sensing video recovery10.1007/s11042-016-3599-4 ↗
37. Discrete Multi-Graph Hashing for Large-scale Visual Search10.1007/s11063-018-9892-7 ↗
38. Sun, H., Gao, C., Zhang, Z., Liao, X., Wang, X., and Yang, J. (2019). High-resolution anisotropic prestack Kirchhoff dynamic focused beam migration. IEEE Sens. J.10.1109/JSEN.2019.2933200 ↗
39. Nguyen, T.T., Pan, J.S., and Dao, T.K. (2019). An Improved Flower Pollination Algorithm for Optimizing Layouts of Nodes in Wireless Sensor Network. IEEE Access, 7.10.1109/ACCESS.2019.2921721 ↗
40. Wang, J., Gao, Y., Liu, W., Sangaiah, A.K., and Kim, H.J. (2018). An Intelligent Data Gathering Schema with Data Fusion Supported for Mobile Sink in WSNs. Int. J. Distrib. Sens. Netw., 15.10.1177/1550147719839581 ↗
41. An improved ant colony optimization-based approach with mobile sink for wireless sensor networks10.1007/s11227-017-2115-6 ↗
42. Energy Efficient Cluster-based Dynamic Routes Adjustment Approach for Wireless Sensor Networks with Mobile Sinks10.1007/s11227-016-1947-9 ↗
43. An Enhanced PEGASIS Algorithm with Mobile Sink Support for Wireless Sensor Networks10.1155/2018/9472075 ↗
44. An Asynchronous Clustering and Mobile Data Gathering Schema based on Timer Mechanism in Wireless Sensor Networks
45. Novel Systolization of Subquadratic Space Complexity Multipliers Based on Toeplitz Matrix-Vector Product Approach10.1109/TVLSI.2019.2903289 ↗
46. Region-Based Compressive Networked Storage with Lazy Encoding10.1109/TPDS.2018.2883550 ↗
47. Alpha-Fraction First Strategy for Hierarchical Wireless Sensor Networks
48. Energy-aware Routing for SWIPT in Multi-hop Energy-constrained Wireless Network10.1109/ACCESS.2018.2820093 ↗