Noise Reduction Techniques in ECG Signal
Keywords:ECG, EMG, SNR, MSE, DA FIR
The problem of noise interference in ECG signals has been addressed in this paper. Specifically, a method has been developed to filter out Electromyography noise (EMG) from ECG signals. A dataset of ECG signals with varying levels of EMG noise has been collected using the MIT-BIH dataset. An algorithm has been designed and implemented using the DA FIR filter coupled with Kaiser windowing technique to filter out the noise. The algorithm has been tested on the collected dataset using MATLAB. The performance of the algorithm has been evaluated by calculating the Signal-to-Noise Ratio (SNR) and the Mean Squared Error (MSE). The effectiveness of the algorithm in reducing the EMG noise in the ECG signals has been demonstrated by the results. The algorithm's limitations and future work were discussed in this paper. Interesting future works could include using other filtering techniques to enhance the performance or deep learning techniques to improve noise cancellation. Overall, the effectiveness of using signal processing techniques to filter out EMG noise from ECG signals has been demonstrated and resulted in clearer and more accurate signals for diagnostic purposes.
C. Saritha, V. Sukanya, and Y. N. Murthy, “ECG Signal Analysis Using Wavelet Transforms,” 2008.
F. Liu, Y. Xu, and Y. Yao, “Highly Efficient Low Noise Solutions in ECG Signals,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Apr. 2022. doi: 10.1088/1742-6596/2246/1/012030.
S. Chatterjee, R. S. Thakur, R. N. Yadav, L. Gupta, and D. K. Raghuvanshi, “Review of noise removal techniques in ECG signals,” IET Signal Processing, vol. 14, no. 9. Institution of Engineering and Technology, pp. 569–590, Dec. 01, 2020. doi: 10.1049/iet-spr.2020.0104.
K. M. Chang, “Arrhythmia ECG noise reduction by ensemble empirical mode decomposition,” Sensors, vol. 10, no. 6, pp. 6063–6080, Jun. 2010, doi: 10.3390/s100606063.
A. Boudraa, J. Cexus, and E. Navale, “DENOISING VIA EMPIRICAL MODE DECOMPOSITION.”
M. Blanco-Velasco, B. Weng, and K. E. Barner, “ECG signal denoising and baseline wander correction based on the empirical mode decomposition,” Comput Biol Med, vol. 38, no. 1, pp. 1–13, Jan. 2008, doi: 10.1016/j.compbiomed.2007.06.003.
N. E. Huang et al., “The empirical mode decomposition and the Hubert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998, doi: 10.1098/rspa.1998.0193.
B. Chen, Y. Li, and N. Zeng, “Centralized Wavelet Multiresolution for Exact Translation Invariant Processing of ECG Signals,” IEEE Access, vol. 7, pp. 42322–42330, 2019, doi: 10.1109/ACCESS.2019.2907249.
C. B. Smith, S. Agaian, and D. Akopian, “A wavelet-denoising approach using polynomial threshold operators,” IEEE Signal Process Lett, vol. 15, pp. 906–909, 2008, doi: 10.1109/LSP.2008.2001815.
M. Alfaouri and K. Daqrouq, “ECG Signal Denoising By Wavelet Transform Thresholding,” Am J Appl Sci, vol. 5, no. 3, pp. 276–281, 2008.
R. Sameni, M. B. Shamsollahi, C. Jutten, and G. D. Clifford, “A nonlinear Bayesian filtering framework for ECG denoising,” IEEE Trans Biomed Eng, vol. 54, no. 12, pp. 2172–2185, Dec. 2007, doi: 10.1109/TBME.2007.897817.
H. D. Hesar and M. Mohebbi, “An adaptive particle weighting strategy for ECG denoising using marginalized particle extended kalman filter: An evaluation in arrhythmia contexts,” IEEE J Biomed Health Inform, vol. 21, no. 6, pp. 1581–1592, Nov. 2017, doi: 10.1109/JBHI.2017.2706298.
H. D. Hesar and M. Mohebbi, “ECG Denoising Using Marginalized Particle Extended Kalman Filter with an Automatic Particle Weighting Strategy,” IEEE J Biomed Health Inform, vol. 21, no. 3, pp. 635–644, May 2017, doi: 10.1109/JBHI.2016.2582340.
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