Abstract
Electrocardiogram (ECG) signals are damaged by various types of noise during acquisition and transmission which may mislead the analysis. In this paper, an automated denoising technique based on time–frequency filtering approach is proposed. The S-transform based time–frequency method with morphological processing is employed to visualize the spectrum of the ECG signal. The time–frequency plane is surface fitted to estimate the noise and then a threshold is used to eliminate it. The proposed method has been assessed with numerous abnormal and normal ECG signals selected from the MIT-BIH normal sinus rhythm database. Several noises with varying signal-to-noise ratio are considered for the simulation study. The results showed that the proposed technique is superior to the existing wavelet-based approach. It significantly reduces the mean square error, percentage root mean square difference and improves the signal-to-noise ratio (SNR). Moreover, at lower SNR condition, the proposed approach efficiently suppresses the noise. In the proposed approach, the requirement of the reference signal is eliminated; and at the same time, the structural information is preserved in the denoised signal.
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Rangayyan, R.M.: Biomedical Signal Analysis: A Case-Study Approach | Wiley. Wiley, Hoboken (2010)
Friesen, G.M.; Jannett, T.C.; Jadallah, M.A.; Yates, S.L.; Quint, S.R.; Nagle, H.T.: A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Trans. Biomed. Eng. 37, 85–98 (1990). https://doi.org/10.1109/10.43620
Sayadi, O.; Shamsollahi, M.B.: Model-based fiducial points extraction for baseline wandered electrocardiograms. IEEE Trans. Biomed. Eng. 55, 347–351 (2008). https://doi.org/10.1109/TBME.2007.899302
Wang, J.; Ye, Y.; Pan, X.; Gao, X.; Zhuang, C.: Fractional zero-phase filtering based on the Riemann–Liouville integral. Signal Process. 98, 150–157 (2014). https://doi.org/10.1016/j.sigpro.2013.11.024
Dliou, A.; Latif, R.; Laaboubi, M.; Maoulainine, F.M.R.: Abnormal ECG signals analysis using non-parametric time–frequency techniques. Arab. J. Sci. Eng. 39, 913–921 (2014). https://doi.org/10.1007/s13369-013-0687-x
Smital, L.; Vítek, M.; Kozumplík, J.; Provazník, I.: Adaptive wavelet wiener filtering of ECG signals. IEEE Trans. Biomed. Eng. 60, 437–445 (2013). https://doi.org/10.1109/TBME.2012.2228482
Luo, Y.; Hargraves, R.H.; Belle, A.; Bai, O.; Qi, X.; Ward, K.R.; Pfaffenberger, M.P.; Najarian, K.: A hierarchical method for removal of baseline drift from biomedical signals: application in ECG analysis. Sci. World J. 2013, 896056 (2013). https://doi.org/10.1155/2013/896056
AlMahamdy, M.; Riley, H.B.: Performance study of different denoising methods for ECG signals. In: Procedia Computer Science, pp. 325–332. Elsevier BV (2014)
Thakor, N.V.; Zhu, Y.S.: Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans. Biomed. Eng. 38, 785–794 (1991). https://doi.org/10.1109/10.83591
George, N.V.; Sahu, S.S.; Mansinha, L.; Tiampo, K.F.; Panda, G.: Time localised band filtering using modified S-transform. In: 2009 International Conference on Signal Processing Systems, ICSPS 2009, pp. 42–46 (2009)
Poornachandra, S.: Wavelet-based denoising using subband dependent threshold for ECG signals. Digit. Signal Process. A Rev. J. 18, 49–55 (2008). https://doi.org/10.1016/j.dsp.2007.09.006
Tzabazis, A.; Eisenried, A.; Yeomans, D.C.; Hyatt, M.I.: Wavelet analysis of heart rate variability: impact of wavelet selection. Biomed. Signal Process. Control. 40, 220–225 (2018). https://doi.org/10.1016/j.bspc.2017.09.027
Sameni, R.; Shamsollahi, M.B.; Jutten, C.; Clifford, G.D.: A nonlinear Bayesian filtering framework for ECG denoising. IEEE Trans. Biomed. Eng. 54, 2172–2185 (2007). https://doi.org/10.1109/TBME.2007.897817
Paul, J.S.; Ramasubba Reddy, M.; Kumar, V.J.: A transform domain SVD filter for suppression of muscle noise artefacts in exercise ECG’s. IEEE Trans. Biomed. Eng. 47, 654–663 (2000). https://doi.org/10.1109/10.841337
Mishra, A.; Singh, A.K.; Sahu, S.S.: ECG signal denoising using time-frequency based filtering approach. In: International Conference on Communication and Signal Processing, ICCSP 2016, pp. 503–507. Institute of Electrical and Electronics Engineers Inc (2016)
Blanco-Velasco, M.; Weng, B.; Barner, K.E.: ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Comput. Biol. Med. 38, 1–13 (2008). https://doi.org/10.1016/j.compbiomed.2007.06.003
So-In, C.; Phaudphut, C.; Rujirakul, K.: Real-time ECG noise reduction with QRS complex detection for mobile health services. Arab. J. Sci. Eng. 40, 2503–2514 (2015). https://doi.org/10.1007/s13369-015-1658-1
Rakshit, M.; Das, S.: An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter. Biomed. Signal Process. Control. 40, 140–148 (2018). https://doi.org/10.1016/j.bspc.2017.09.020
García, C.A.; Otero, A.; Vila, X.; Márquez, D.G.: A new algorithm for wavelet-based heart rate variability analysis. Biomed. Signal Process. Control. 8, 542–550 (2013). https://doi.org/10.1016/j.bspc.2013.05.006
Erçelebi, E.: Electrocardiogram signals de-noising using lifting-based discrete wavelet transform. Comput. Biol. Med. 34, 479–493 (2004). https://doi.org/10.1016/S0010-4825(03)00090-8
Li, S.; Lin, J.: The optimal de-noising algorithm for ECG using stationary wavelet transform. In: 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 469–473 (2009)
Das, M.K.; Ari, S.: Analysis of ECG signal denoising method based on S-transform. IRBM. 34, 362–370 (2013). https://doi.org/10.1016/j.irbm.2013.07.012
Vargas, R.N.; Veiga, A.C.P.: Electrocardiogram signal denoising by a new noise variation estimate. Res. Biomed. Eng. 36, 13–20 (2020). https://doi.org/10.1007/s42600-019-00033-y
Stockwell, R.G.: A basis for efficient representation of the S-transform. Digit. Signal Process. A Rev. J. 17, 371–393 (2007). https://doi.org/10.1016/j.dsp.2006.04.006
Stockwell, R.G.: Localization of the complex spectrum: the s transform. IEEE Trans. Signal Process. 44, 993 (1996). https://doi.org/10.1109/78.492555
Raković, P.; Sejdić, E.; Stanković, L.J.; Jiang, J.: Time–frequency signal processing approaches with applications to heart sound analysis. Comput. Cardiol. 33, 197–200 (2006)
Pinnegar, C.R.; Eaton, D.W.: Application of the S transform to prestack noise attenuation filtering. J. Geophys. Res. Solid Earth. (2003). https://doi.org/10.1029/2002jb002258
Dash, P.K.; Panigrahi, B.K.; Panda, G.: Power quality analysis using S-transform. IEEE Trans. Power Deliv. 18, 406–411 (2003). https://doi.org/10.1109/TPWRD.2003.809616
McFadden, P.D.; Cook, J.G.; Forster, L.M.: Decomposition of gear vibration signals by the generalized S transform. Mech. Syst. Signal Process. 13, 691–707 (1999). https://doi.org/10.1006/mssp.1999.1233
Kabir, M.A.; Shahnaz, C.: Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomed. Signal Process. Control. 7, 481–489 (2012). https://doi.org/10.1016/j.bspc.2011.11.003
Donoho, D.: Denoising by soft thresholding. IEEE Trans. Inform. Theory 41, 612–627 (1995)
Chu, C.H.H.; Delp, E.J.: Impulsive noise suppression and background normalization of electrocardiogram signals using morphological operators. IEEE Trans. Biomed. Eng. 36, 262–273 (1989). https://doi.org/10.1109/10.16474
Sahu, S.S.; Panda, G.; George, N.V.: An improved S-transform for time-frequency analysis. In: 2009 IEEE International Advance Computing Conference, IACC 2009, pp. 315–319 (2009)
Moody, G.B.; Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20, 45–50 (2001)
Moody, G.B.; Muldrow, W.E.; Mark, R.G.: The MIT-BIH noise stress test database. In: Computers in Cardiology, pp. 381–384 (1984)
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This work has been carried out in Signal Processing Lab, Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Ranchi, India. The study has been funded by NPIU, MHRD, Govt. of India Grant # 1-5737336180.
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Mishra, A., Sahu, S.S., Sharma, R. et al. Denoising of Electrocardiogram Signal Using S-Transform Based Time–Frequency Filtering Approach. Arab J Sci Eng 46, 9515–9525 (2021). https://doi.org/10.1007/s13369-021-05333-z
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DOI: https://doi.org/10.1007/s13369-021-05333-z