ECG SIGNAL DENOISING: AN INTEGRATED FILTERING APPROACH FOR IMPROVED SIGNAL QUALITY

Fatima W.  Abdullah; Hadeel N.  Abdullah

doi:10.30572/2018/KJE/170116

Authors

Fatima W. Abdullah Electrical Engineering Department, University of Technology- Iraq-Baghdad, Assistant Chief Engineer, Ministry of Electricity, Operation and Control Office- Iraq-Baghdad, Iraq
M.Sc. Fatima W. Abdullah Electrical Engineering Department, University of Technology- Iraq-Baghdad, Iraq

DOI:

https://doi.org/10.30572/2018/KJE/170116

Keywords:

Electrocardiogram, Signal Denoising, Butterworth Filter, Gaussian Window Filter, Baseline Wander, Power Line Interference

Abstract

The electrocardiogram is an essential tool in biomedical diagnostics for detecting cardiovascular diseases. However, the non-stationary characteristics of ECG signals frequently result in noise contamination, leading to inaccuracies that prevent diagnosis and analysis. The proposed methodology improves electrocardiogram signal processing by preserving essential diagnostic features, including the ST segment, which exists in the low-frequency region and is susceptible to noise interference. The proposed methodology involves three key steps: first, a bandpass filter is used to separate the electrocardiogram frequency band within 0.5–40 Hz; second, noise reduction techniques like Savitzky-Golay, Butterworth, and Gaussian window filters are used to eliminate of Electromyogram, Baseline Wander, and Power Line Interference noise; and third, segmenting the processed data for further analysis. These steps make the signal stronger and more reliable, which leads to more accurate diagnostic results and easier advanced feature extraction. The performance metrics with MATLAB R2023a and the MIT-BIH database showed a notable increase in the signal-to-noise ratio going up to 44.13 dB and the mean squared error going down to 0.0000

Downloads

Download data is not yet available.

References

ABDULWAHAB, S. S., K.KHLEAF, H. & H.JASSIM, M. 2021. EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and cubic SVM. Journal of Physics: Conference Series. DOI: https://doi.org/10.1088/1742-6596/1973/1/012056

ALRUBEI, M. A. T. & DMITRIEVICH, P. A. 2023. An Approach for Single-Tone Frequency Estimation Using Dft Interpolation with Parzen Windowing. Kufa Journal of Engineering, 14, 93-104. DOI: https://doi.org/10.30572/2018/KJE/140307

AZZOUZ, A., BENGHERBIA, B., WIRA, P., ALAOUI, N., SOUAHLIA, A., MAAZOUZ, M. & HENTABELI, H. 2024. An efficient ECG signals denoising technique based on the combination of particle swarm optimisation and wavelet transform. Heliyon, 10, e26171. DOI: https://doi.org/10.1016/j.heliyon.2024.e26171

BING, P., LIU, W., ZHAI, Z., LI, J., GUO, Z., XIANG, Y., HE, B. & ZHU, L. 2024. A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme. Front Cardiovasc Med, 11, 1277123. DOI: https://doi.org/10.3389/fcvm.2024.1277123

CHATTERJEE, S., THAKUR, R. S., YADAV, R. N., GUPTA, L. & RAGHUVANSHI, D. K. 2020. Review of noise removal techniques in ECG signals. IET Signal Processing, 14, 569-590. DOI: https://doi.org/10.1049/iet-spr.2020.0104

CHE, C., ZHANG, P., ZHU, M., QU, Y. & JIN, B. 2021. Constrained transformer network for ECG signal processing and arrhythmia classification. BMC Med Inform Decis Mak, 21, 184. DOI: https://doi.org/10.1186/s12911-021-01546-2

CHIENG, T. M., HAU, Y. W., BIN OMAR, Z. & LIM, C. W. 2020. Qualitative and Quantitative Performance Comparison of ECG Noise Reduction and Signal Enhancement Method based on Various Digital Filter Designs and Discrete Wavelet Transform. International Journal of Computing and Digital Systems, 9, 553-565. DOI: https://doi.org/10.12785/ijcds/090404

D.YUSUF, S., MADUAKOLAM, F. C., UMAR, I. & Z.LOKO, A. 2020. Analysis of Butterworth Filter For Electrocardiogram De-Noising Using Daubechies Wavelets. International Journal of Electronics and Communication Engineering, 7, 8-13. DOI: https://doi.org/10.14445/23488549/IJECE-V7I4P103

FAIZ, M. M. U. & KALE, I. 2022. Removal of multiple artifacts from ECG signal using cascaded multistage adaptive noise cancellers. Array, 14. DOI: https://doi.org/10.1016/j.array.2022.100133

GABER, R. K., SALIH, R. S. & JIAAD, S. M. 2025. Artificial Neural Network (ANN) based Proportional Integral Derivative (PID) for Arm Rehabilitation Device. Kufa Journal of Engineering, 16. DOI: https://doi.org/10.30572/2018/KJE/160106

HERMAWAN, I., SEVANI, N., ABKA, A. F. & JATMIKO, W. 2024. Denoising Ambulatory Electrocardiogram Signal Using Interval-Dependent Thresholds-based Stationary Wavelet Transform. International Journal on Informatics Visualization. DOI: https://doi.org/10.62527/joiv.8.2.2428

K, M. & SYED, K. 2024. Arrhythmia classification for non-experts using infinite impulse response (IIR)-filter-based machine learning and deep learning models of the electrocardiogram. PeerJ Comput Sci, 10, e1774. DOI: https://doi.org/10.7717/peerj-cs.1774

KRISHNA CHAITANYA, M. & SHARMA, L. D. 2022. Electrocardiogram signal filtering using circulant singular spectrum analysis and cascaded Savitzky-Golay filter. Biomedical Signal Processing and Control, 75. DOI: https://doi.org/10.1016/j.bspc.2022.103583

LESKI, J. M. 2021. Robust nonlinear aggregation operator for ECG powerline interference reduction. Biomedical Signal Processing and Control, 69. DOI: https://doi.org/10.1016/j.bspc.2021.102675

LIU, B. & LI, Y. 2021. ECG signal denoising based on similar segments cooperative filtering. Biomedical Signal Processing and Control, 68. DOI: https://doi.org/10.1016/j.bspc.2021.102751

LIU, M., HAO, H., XIONG, P., LIN, F., HOU, Z. & LIU, X. 2017. Constructing a Guided Filter by Exploiting the Butterworth Filter for ECG Signal Enhancement. Journal of Medical and Biological Engineering, 38, 980-992. DOI: https://doi.org/10.1007/s40846-017-0350-1

MADUAKOLAM, F. C., YUSUF, S. D. & UMAR, I. 2022. Analysis of Savitzky-Golay Filter for Electrocardiogram De-Noising Using Daubechies Wavelets. EDUCATUM Journal of Science, Mathematics and Technology, 1. DOI: https://doi.org/10.53797/aseana.v1i2.3.2021

MANJU.B.R & SNEHA.M.R 2020. ECG denoising using Wiener filter and Kalman filter. Procedia Computer Science,171. DOI: https://doi.org/10.1016/j.procs.2020.04.029

MISHRA, A., SAHU, S. S., SHARMA, R. & MISHRA, S. K. 2021. Denoising of Electrocardiogram Signal Using S-Transform Based Time–Frequency Filtering Approach. Arabian Journal for Science and Engineering, 46, 9515-9525. DOI: https://doi.org/10.1007/s13369-021-05333-z

MOHAMMED, H. A., KAREEM, S. W. & MOHAMMED, A. S. 2022. A Comparative Evaluation of Deep Learning Methods in Digital Image Classification. Kufa Journal of Engineering, 13, 53-69. DOI: https://doi.org/10.30572/2018/KJE/130405

N. HUSSIN, K., K. NAHAR, A. & KAREEM KHLEAF, H. 2022. A hybrid bat-genetic algorithm for improving the visual quality of medical images. Indonesian Journal of Electrical Engineering and Computer Science, 28. DOI: https://doi.org/10.11591/ijeecs.v28.i1.pp220-226

OLIVEIRA, B. R. D., DUARTE, M. A. Q., ABREU, C. C. E. D. & VIEIRA FILHO, J. 2018. A wavelet-based method for power-line interference removal in ECG signals. Research on Biomedical Engineering, 34, 73-86. DOI: https://doi.org/10.1590/2446-4740.01817

PŁAWIAK, P. 2018. Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system. Expert Systems with Applications, 92, 334-349. DOI: https://doi.org/10.1016/j.eswa.2017.09.022

RAWASH, Y. Z., AL‐NAAMI, B., ALFRAIHAT, A. & OWIDA, H. A. 2024. Advanced Low-Pass Filters for Signal Processing: A Comparative Study on Gaussian, Mittag-Leffler, and Savitzky-Golay Filters. Mathematical Modelling of Engineering Problems, 11, 1841-1850. DOI: https://doi.org/10.18280/mmep.110713

SAMANN, F. & SCHANZE, T. 2019. An efficient ECG Denoising method using Discrete Wavelet with Savitzky-Golay filter. Current Directions in Biomedical Engineering, 5, 385-387. DOI: https://doi.org/10.1515/cdbme-2019-0097

SINNOOR, M. & JANARDHAN, S. K. 2022. An ECG Denoising Method Based on Hybrid MLTP-EEMD Model. International Journal of Intelligent Engineering and Systems, 15. DOI: https://doi.org/10.22266/ijies2022.0228.52

TALIB, M., ABDULLAH, A., ABDULLAH, A. & HAMZAH, B. 2021. Twin Fetus Ecg Signal Extraction Based on Temporal Predictability. Kufa Journal of Engineering, 11, 35-51. DOI: https://doi.org/10.30572/2018/KJE/110103

WAHEED, M. 2023. Pre-processing of ECG signal based on multistage filters. Journal of Integrated Circuits and Systems, 18, 1-6. DOI: https://doi.org/10.29292/jics.v18i2.698