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Optimized Denoising of ECG and EEG Signals Using Discrete Wavelet Transform: A Comparative Study of Wavelet Types and Thresholding Techniques

Authors:

Mohammed Ahmed Moh., Yasin Yousif Al-Aboosi, Hussein A. Abdualnabi, Hussein Yasin Al-Aboosi

DOI:

  • https://doi.org/10.65983/ijhec.2026.03.0005

Abstract:

Biomedical signals, specifically Electrocardiograms (ECG) and Electroencephalograms (EEG), using Particle Swarm Optimization (PSO) integrated with Discrete Wavelet Transform (DWT). Unlike traditional methods that rely on fixed thresholding, the proposed approach dynamically optimizes the selection of wavelet families (Daubechies, Coiflet, Symlet), decomposition levels, and thresholding parameters to maximize the Signal-to-Noise Ratio (SNR) while minimizing the Mean Squared Error (MSE). To ensure statistical validation, the framework was tested on 30 diverse records from the MIT-BIH and PhysioNet databases, corrupted with synthetic noise to simulate clinical interference. Results demonstrate that the PSO-optimized Symlet 8 (sym8) configuration significantly outperforms standard DWT methods, achieving an average SNR improvement of 19.88 dB for ECG and 15.57 dB for EEG. Statistical significance was confirmed via a paired t-test (p < 0.05), proving the robustness of the optimized model in preserving critical diagnostic features like the QRS complex and spike-wave discharges. This study bridges the gap between theoretical denoising and automated clinical diagnostics, offering a scalable model for real-time patient monitoring systems.

Keywords:

Biomedical Signal Processing, DWT, ECG Denoising, EEG Denoising, SNR

Refference:

1. Sörnmo, Leif, and Pablo Laguna. “Electrocardiogram (ECG) Signal Processing.” Wiley Encyclopedia of Biomedical Engineering, 2006, pp. 1–16. 10.1002/9780471740360.ebs1482
2. Del Pozo-Banos, Marcos, et al. “Electroencephalogram Subject Identification: A Review.” Expert Systems with Applications, vol. 41, no. 15, 2014, pp. 6537–6554. 10.1016/j.eswa.2014.05.013
3. Reilly, Richard B., and T. C. Lee. “Electrograms (ECG, EEG, EMG, EOG).” Technology and Health Care, vol. 18, no. 6, 2010, pp. 443–458. 10.3233/THC-2010-0594
4. Olkkonen, Hannu. Discrete Wavelet Transforms: Biomedical Applications. BoD–Books on Demand, 2011.
5. Rafiee, Jamal, et al. “Wavelet Basis Functions in Biomedical Signal Processing.” Expert Systems with Applications, vol. 38, no. 5, 2011, pp. 6190–6201. 10.1016/j.eswa.2010.11.078

6. Patil, P. B., and M. S. Chavan. “A Wavelet Based Method for Denoising of Biomedical Signal.” International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME), IEEE, 2012. 10.1109/PRIME.2012.6208304
7. Ali, M. A., S. Ali, and A. Khorsheed. “ECG Signal Denoising Using Discrete Wavelet Transform.” Journal of Duhok University, vol. 26, no. 2, 2023, pp. 450–463. 10.26682/ajuod.2023.26.2.41
8. Tripathi, P. M., et al. “A Novel Approach for Real-Time ECG Signal Denoising Using Fourier Decomposition Method.” Research on Biomedical Engineering, vol. 38, no. 4, 2022, pp. 1037–1049. 10.1007/s42600-021-00163-5
9. Xia, Y.-X., et al. “Strain Signal Denoising in Bridge SHM: A Comparative Analysis of MODWT and Other Techniques.” Journal of Infrastructure Intelligence and Resilience, 2025, p. 100155. 10.1016/j.iintel.2025.100155
10. Polat, C., and M. S. Özerdem. “Introduction to Wavelets and Their Applications in Signal Denoising.” Bitlis Eren University Journal of Science and Technology, vol. 8, no. 1, 2018, pp. 1–10.
11. Grobbelaar, M., et al. “A Survey on Denoising Techniques of Electroencephalogram Signals Using Wavelet Transform.” Signals, vol. 3, no. 3, 2022, pp. 577–586. 10.3390/signals3030033
12. Kaushik, G., H. Sinha, and L. Dewan. “Biomedical Signals Analysis by DWT Signal Denoising with Neural Networks.” Journal of Theoretical & Applied Information Technology, vol. 62, no. 1, 2014.
13. Azzouz, A., et al. “An Efficient ECG Signals Denoising Technique Based on the Combination of Particle Swarm Optimisation and Wavelet Transform.” Heliyon, vol. 10, no. 5, 2024. 10.1016/j.heliyon.2024.e26839
14. Sharma, R. “EEG Signal Denoising Based on Wavelet Transform.” International Conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE, 2017. 10.1109/ICECA.2017.8212714
15. Awal, M. A., et al. “An Adaptive Level Dependent Wavelet Thresholding for ECG Denoising.” Biocybernetics and Biomedical Engineering, vol. 34, no. 4, 2014, pp. 238–249. 10.1016/j.bbe.2014.07.002
16. Ara, I., M. N. Hossain, and S. Y. Mahbub. “Baseline Drift Removal and De-Noising of the ECG Signal Using Wavelet Transform.” International Journal of Computer Applications, vol. 95, no. 16, 2014. 10.5120/16520-6657
17. Kaur, C., P. Singh, and S. Sahni. “EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach.” Basic and Clinical Neuroscience, vol. 12, no. 4, 2021, p. 465. https://doi.org/10.32598/bcn.2021.2031.1
18. Zhang, L., et al. “MFC-PINN: A Method to Improve the Accuracy and Robustness of Acoustic Emission Source Planar Localization.” Measurement, vol. 235, 2024, p. 114995. 10.1016/j.measurement.2024.114995
19. Alessio, Sergio M. “Discrete Wavelet Transform (DWT).” Digital Signal Processing and Spectral Analysis for Scientists: Concepts and Applications, 2016, pp. 645–714. 10.1201/9781315361901
20. Sundararajan, D. Discrete Wavelet Transform: A Signal Processing Approach. John Wiley & Sons, 2016. 10.1002/9781119051893
21. Gandhi, T., B. K. Panigrahi, and S. Anand. “A Comparative Study of Wavelet Families for EEG Signal Classification.” Neurocomputing, vol. 74, no. 17, 2011, pp. 3051–3057. 10.1016/j.neucom.2011.02.017
22. Dautov, Ç. P., and M. S. Özerdem. “Wavelet Transform and Signal Denoising Using Wavelet Method.” Signal Processing and Communications Applications Conference (SIU), IEEE, 2018. 10.1109/SIU.2018.8404291
23. Othman, G., and D. Q. Zeebaree. “The Applications of Discrete Wavelet Transform in Image Processing: A Review.” Journal of Soft Computing and Data Mining, vol. 1, no. 2, 2020, pp. 31–43. 10.30880/jscdm.2020.01.02.004
24. Livstone, M. M. Wavelets: A Conceptual Overview. 1994.
25. Jallouli, M., et al. “Toward New Multi-Wavelets: Associated Filters and Algorithms.” Soft Computing, vol. 25, 2021, pp. 14059–14079. 10.1007/s00500-021-05888-0
26. Hu, Z., and L. Liu. “Applications of Wavelet Analysis in Differential Propagation Phase Shift Data De-Noising.” Advances in Atmospheric Sciences, vol. 31, 2014, pp. 825–835. 10.1007/s00376-013-3059-8
27. Alfaouri, M., and K. Daqrouq. “ECG Signal Denoising by Wavelet Transform Thresholding.” American Journal of Applied Sciences, vol. 5, no. 3, 2008, pp. 276–281. 10.3844/ajassp.2008.2

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