International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 16, No. 4, 8 Aug. 2024
Cover page and Table of Contents: PDF (size: 2842KB)
50Hz Power Line Interference Removal from an Electrocardiogram Signal Using a VME-DWT-Based Frequency Extraction and Filtering Approach
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Author(s)
Index Terms
Correlation coefficient, Discrete wavelet transform, power line interference, signal-to-noise ratio, mean square error, variational mode extraction
Abstract
Removing undesirable artifacts in electrocardiogram signals is essential for biological signal processing as the signal gets distorted and makes appropriate investigation challenging. A primary source of distortion affecting recordings is the 50Hz power line interference. To get a high-quality recording, we used a filtering method based on an efficient decomposition technique known as variational mode extraction. This approach is similar to the variational mode decomposition methodology but with a few alterations in mathematical computation. First, it extracts the noise efficiently in a specific frequency band. Then, we apply the discrete wavelet transform to the signal, employing soft thresholding. As a result, it eliminates the extra noise and filters the electrocardiogram signal. We evaluated the efficacy of our proposed method using an arrhythmia database. Furthermore, we compared recent decomposition methods on six random signals using signal-to-noise ratios, mean square errors, correlation coefficients, and other signal features. Our method also efficiently eliminates varying amplitude of powerline noise and finally outperforms decomposition strategies regarding noise reduction and processing complexity across all signal parameters.
Cite This Paper
Pavan G. Malghan, Malaya Kumar Hota, "50Hz Power Line Interference Removal from an Electrocardiogram Signal Using a VME-DWT-Based Frequency Extraction and Filtering Approach", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.16, No.4, pp. 56-73, 2024. DOI:10.5815/ijigsp.2024.04.05
Reference
[1]Ramirez, F. Daniel, et al. “High-resolution mapping of reentrant atrial tachycardias: Relevance of low bipolar voltage.” Heart Rhythm 20.3 (2023): 430-437.
[2]Varghees, V. Nivitha, Hua Cao, and Laurent Peyrodie. “Variational Mode Decomposition Based Simultaneous R Peak Detection and Noise Suppression for Automatic ECG Analysis.” IEEE Sensors Journal (2023).
[3]Huang, Shaobin, Peng Wang, and Rongsheng Li. “Noise ECG generation method based on generative adversarial network.” Biomedical Signal Processing and Control 81 (2023): 104444.
[4]Wesselius, Fons J., et al. “Digital biomarkers and algorithms for detection of atrial fibrillation using surface electrocardiograms: A systematic review.” Computers in Biology and Medicine (2021): 104404.
[5]Malghan, Pavan G., and Malaya Kumar Hota. “A review on ECG filtering techniques for rhythm analysis.” Research on Biomedical Engineering 36.2 (2020): 171-186.
[6]Aruna, V. B. K. L., and E. Chitra. “An ECG Signal Compression Technique Using Fast Normalised Least Mean Square Algorithm.” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization (2023): 1-8.
[7]Rakshit, Manas, and Susmita Das. “An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter.” Biomedical signal processing and control 40 (2018): 140-148.
[8]Malik, Shahid A., Shabir A. Parah, and Bilal A. Malik. “Power line noise and baseline wander removal from ECG signals using empirical mode decomposition and lifting wavelet transform technique.” Health and Technology 12.4 (2022): 745-756.
[9]Mohguen, Wahiba, and Saad Bouguezel. “Denoising the ECG signal using ensemble empirical mode decomposition.” Engineering, Technology & Applied Science Research 11.5 (2021): 7536-7541.
[10]Sraitih, Mohamed, and Younes Jabrane. “A denoising performance comparison based on ECG Signal Decomposition and local means filtering.” Biomedical Signal Processing and Control 69 (2021): 102903.
[11]Jain, Shweta, Varun Bajaj, and Anil Kumar. “Effective denoising of ECG by optimised adaptive thresholding on noisy modes.” IET Science, Measurement & Technology 12.5 (2018): 640-644.
[12]Hossain, Md-Billal, et al. “A robust ECG denoising technique using variable frequency complex demodulation.” Computer Methods and Programs in Biomedicine 200 (2021): 105856.
[13]Dwivedi, Atul Kumar, et al. “Noise reduction in ECG signal using combined ensemble empirical mode decomposition method with stationary wavelet transform.” Circuits, Systems, and Signal Processing 40.2 (2021): 827-844.
[14]Birok, Rajesh. “ECG denoising using artificial neural networks and complete ensemble empirical mode decomposition.” Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12.2 (2021): 2382-2389.
[15]Malhotra, Vikas, and Mandeep Kaur Sandhu. “Electrocardiogram signals denoising using improved variational mode decomposition.” Journal of Medical Signals and Sensors 11.2 (2021): 100-107.
[16]Zeng, Wei, and Chengzhi Yuan. “ECG arrhythmia classification based on variational mode decomposition, Shannon energy envelope, and deterministic learning.” International Journal of Machine Learning and Cybernetics (2021): 1-26.
[17]Singh, Pratik, and Gayadhar Pradhan. “Variational mode decomposition-based ECG denoising using non-local means and wavelet domain filtering.” Australasian Physical & Engineering Sciences in Medicine 41.4 (2018): 891-904.
[18]Tan, Chunyu, et al. “A novel feature representation approach for single-lead heartbeat classification based on adaptive Fourier decomposition.” International Journal of Wavelets, Multiresolution and Information Processing 19.05 (2021): 2150010.
[19]Swain, Sushree Satvatee, Dipti Patra, and Yengkhom Omesh Singh. “Automated detection of myocardial infarction in ECG using modified Stockwell transform and phase distribution pattern from time-frequency analysis.” Biocybernetics and Biomedical Engineering 40.3 (2020): 1174-1189.
[20]Malleswari, Pinjala N., Hima Bindu, and K. Satya Prasad. “An Improved Denoising of Electrocardiogram Signals Based on Wavelet Thresholding.” Journal of Biomimetics, Biomaterials and Biomedical Engineering. Vol. 51. Trans Tech Publications Ltd, 2021.
[21]Kumar, Ashish, et al. “Stationary wavelet transform based ECG signal denoising method.” ISA transactions 114 (2021): 251-262.
[22]Malghan, Pavan G., and Malaya Kumar Hota. “Grasshopper optimisation algorithm based improved variational mode decomposition technique for muscle artifact removal in ECG using dynamic time warping.” Biomedical Signal Processing and Control 73 (2022): 103437.
[23]Nazari, Mojtaba, and Sayed Mahmoud Sakhaei. “Variational mode extraction: A new efficient method to derive respiratory signals from ECG.” IEEE Journal of Biomedical and Health Informatics 22.4 (2017): 1059-1067.
[24]Shahbakhti, Mohammad, et al. “VME-DWT: an efficient algorithm for detection and elimination of eye blink from short segments of single EEG channel.” IEEE Transactions on Neural Systems and Rehabilitation Engineering 29 (2021): 408-417.
[25]Li, Wei. “Wavelets for electrocardiogram: overview and taxonomy.” IEEE Access 7 (2018): 25627-25649.
[26]Chatterjee, Shubhojeet, et al. “Review of noise removal techniques in ECG signals.” IET Signal Processing 14.9 (2020): 569-590.
[27]Awal, Md Abdul, et al. “An adaptive level-dependent wavelet thresholding for ECG denoising.” Biocybernetics and Biomedical Engineering 34.4 (2014): 238-249.
[28]Kaur, Chamandeep, et al. “EEG Signal denoising using hybrid approach of Variational Mode Decomposition and wavelets for depression.” Biomedical Signal Processing and Control 65 (2021): 102337.
[29]Dora, Chinmayee, and Pradyut Kumar Biswal. “An improved algorithm for efficient ocular artifact suppression from frontal EEG electrodes using VMD.” Biocybernetics and Biomedical Engineering 40.1 (2020): 148-161.
[30]Li, Juan, et al. “Research on VMD based adaptive denoising method applied to water supply pipeline leakage location.” Measurement 151 (2020): 107153.
[31]Moody, George B., and Roger G. Mark. “The impact of the MIT-BIH arrhythmia database.” IEEE engineering in medicine and biology magazine 20.3 (2001): 45-50.