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Dual-stage SVD basis approach for ECG signal associated noise removal

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Abstract

ECG signals may give misleading medical information unless the accompanying noise established during the acquisition process is removed. In this work, a new algorithm is developed to significantly remove muscle artifact, electrode motion noise and additive white Gaussian noise which usually exist in ECG signals. The singular value decomposition (SVD) has been already exploited to clean the ECG noisy signals, but the denoised signals usually suffer from residual noise due to the dispute in the strategies followed to determine signal and noise eigencomponents. This paper presents a new algorithm which utilizes applying SVD in double stages. The first stage is a regular SVD whose data output is divided into overlapped groups. Each group is then arranged in a Hankel matrix to expand the processing space. Thereafter, SVD is applied again on each Hankel matrix to track the rest of the noise which becomes evident by enlarging the processing space. The efficiency of the proposed system was evaluated by calculating signal-to-noise ratio (SNR), mean square error (MSE) and percent root mean square difference (PRD). Promising results were obtained during conducting the simulations with input ECG signals of 5 dB and 10 dB SNRs. Utilizing the proposed algorithm at 5 dB input SNR, the obtained results reached up to 18.31 dB, 0.331e−3 and 12.14% for SNR, MSE and PRD, respectively. Finally, the excellence performance of the proposed system was clearly dominant over other ECG signal denoising techniques.

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References

  1. McSharry, P.E., Clifford, G.D., Tarassenko, L., Smith, L.A.: A dynamical model for generating synthetic electrocardiogram signals. IEEE Trans. Biomed. Eng. 50(3), 289–294 (2003). https://doi.org/10.1109/TBME.2003.808805

    Article  Google Scholar 

  2. Tracey, B.H., Eric, L.M.: Nonlocal means denoising of ECG signals. IEEE Trans. Biomed. Eng. 59(9), 2383–2386 (2012). https://doi.org/10.1109/TBME.2012.2208964

    Article  Google Scholar 

  3. Wang, Z., Wan, F., Wong, C.M., Zhang, L.: Adaptive Fourier decomposition based ECG denoising. Comput. Biol. Med. 77, 195–205 (2017). https://doi.org/10.1016/j.compbiomed.2016.08.013

    Article  Google Scholar 

  4. Sharma, R.R., Kumar, M., Pachori, R.B.: Joint time-frequency domain-based CAD disease sensing system using ECG signals. IEEE Sens. J. 19(10), 3912–3920 (2019). https://doi.org/10.1109/JSEN.2019.2894706

    Article  Google Scholar 

  5. Hesar, H.D., Mohebbi, M.: ECG denoising using marginalized particle extended kalman filter with an automatic particle weighting strategy. IEEE J. Biomed. Health Inform. 21(3), 635–644 (2016). https://doi.org/10.1109/JBHI.2016.2582340

    Article  Google Scholar 

  6. Singh, G., Gagandeep K., Vineet K.: ECG denoising using adaptive selection of IMFs through EMD and EEMD. In: 2014 International Conference on Data Science & Engineering (ICDSE), Aug. 26–28, pp. 228–231 (2014). https://doi.org/10.1109/ICDSE.2014.6974643

  7. Nguyen, P., Kim, J.M.: Adaptive ECG denoising using genetic algorithm-based thresholding and ensemble empirical mode decomposition. Inf. Sci. 373, 499–511 (2016). https://doi.org/10.1016/j.ins.2016.09.033

    Article  Google Scholar 

  8. Yin, J., Xiaoqi C., Pengyuan Z., Lei S., Ji L., Hongli L.: Research on ECG signal denoising by combination of EEMD and NLM. In: 2020 Chinese Control And Decision Conference (CCDC), Hefei, China, Aug. 22–24, pp. 5033–5038 (2020). https://doi.org/10.1109/CCDC49329.2020.9164704

  9. Kabir, M.A., Celia, S.: Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomed. Signal Process. Control 7(5), 481–489 (2012). https://doi.org/10.1016/j.bspc.2011.11.003

    Article  Google Scholar 

  10. Vargas, R.N., Veiga, A.C.P.: Empirical mode decomposition, viterbi and wavelets applied to electrocardiogram noise removal. Circuits Syst Signal Process. 40(2), 691–718 (2021). https://doi.org/10.1007/s00034-020-01489-5

    Article  Google Scholar 

  11. Lin, H.Y., Liang, S.Y., Ho, Y.L., Lin, Y.H., Ma, H.P.: Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals. Innov Res Bio-Med Eng 35(6), 351–361 (2014). https://doi.org/10.1016/j.irbm.2014.10.004

    Article  Google Scholar 

  12. Zhang, J., Lin J., Li, X., Wang, W.: ECG signals denoising method based on improved wavelet threshold algorithm. In: 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Xi'an, China, Oct. 3–5, pp. 1779–1784 (2016). https://doi.org/10.1109/IMCEC.2016.7867525

  13. Awal, M.A., Mostafa, S.S., Ahmad, M., Rashid, M.A.: An adaptive level dependent wavelet thresholding for ECG denoising. Biocybern Biomed Eng 34(4), 238–249 (2014). https://doi.org/10.1016/j.bbe.2014.03.002

    Article  Google Scholar 

  14. Xiong, P., Wang, H., Liu, M., Zhou, S., Hou, Z., Liu, X.: ECG signal enhancement based on improved denoising auto-encoder. Eng. Appl. Artif. Intell. 52, 194–202 (2016). https://doi.org/10.1016/j.engappai.2016.02.015

    Article  Google Scholar 

  15. Jenkal, W., Latif, R., Toumanari, A., Dliou, A., El B’charri, O., Maoulainine, F.M.: An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform. Biocybern Biomed Eng 36(3), 499–508 (2016). https://doi.org/10.1016/j.bbe.2016.04.001

    Article  Google Scholar 

  16. García, M., Martínez-Iniesta, M., Ródenas, J., Rieta, J.J., Alcaraz, R.: A novel wavelet-based filtering strategy to remove powerline interference from electrocardiograms with atrial fibrillation. Physiol. Meas. 39(11), 115006 (2018). https://doi.org/10.1088/1361-6579/aae8b1

    Article  Google Scholar 

  17. Bing, P., Liu, W., Wang, Z., Zhang, Z.: Noise reduction in ECG signal using an effective hybrid scheme. IEEE Access 8, 160790–160801 (2020). https://doi.org/10.1109/ACCESS.2020.3021068

    Article  Google Scholar 

  18. Wang, X., You, Z., Minglei, S., Yinglong, W., Anming, D.: ECG baseline wander correction and denoising based on sparsity. IEEE Access 7, 31573–31585 (2019). https://doi.org/10.1109/ACCESS.2019.2902616

    Article  Google Scholar 

  19. Gualsaquí, M.M.V., Vizcaíno, E.I.P., Flores-Calero, M.J., Carrera, E.V.: ECG signal denoising through kernel principal components. In: 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), Cusco, Peru, Aug. 15–18, pp. 1–4 (2017). https://doi.org/10.1109/INTERCON.2017.8079670

  20. Chen, X., Lin, J., Huang, C., He, L.: A novel method based on adaptive periodic segment matrix and singular value decomposition for removing EMG artifact in ECG signal. Biomed. Signal Process. Control 62, 102060 (2020). https://doi.org/10.1016/j.bspc.2020.102060

    Article  Google Scholar 

  21. Wang, G., Yang, L., Liu, M., Yuan, X., Xiong, P., Lin, F., Liu, X.: ECG signal denoising based on deep factor analysis. Biomed. Signal Process. Control 57, 101824 (2020). https://doi.org/10.1016/j.bspc.2019.101824

    Article  Google Scholar 

  22. Keshavan, R.H., Montanari, A., Oh, S.: Matrix completion from a few entries. IEEE Trans. Inf. Theory 56(6), 2980–2998 (2010). https://doi.org/10.1109/TIT.2010.2046205

    Article  MathSciNet  MATH  Google Scholar 

  23. Gavish, M., Donoho, D.L.: The optimal hard threshold for singular values is 4/3. IEEE Trans. Inf. Theory 60(8), 5040–5053 (2014). https://doi.org/10.1109/TIT.2014.2323359

    Article  MathSciNet  MATH  Google Scholar 

  24. Zhao, W., Lv, Y., Liu, Q., Qin, B.: Detail-preserving image denoising via adaptive clustering and progressive PCA thresholding. IEEE Access 6, 6303–6315 (2017). https://doi.org/10.1109/ACCESS.2017.2780985

    Article  Google Scholar 

  25. Chatterjee, S., Thakur, R.S., Yadav, R.N., Gupta, L., Raghuvanshi, D.K.: Review of noise removal techniques in ECG signals. IET Signal Proc. 14(9), 569–590 (2020). https://doi.org/10.1049/iet-spr.2020.0104

    Article  Google Scholar 

  26. Kher, R.: Signal processing techniques for removing noise from ECG signals. J. Biomed. Eng. Res. 3, 1–9 (2019). https://doi.org/10.17303/jber.2019.3.101

    Article  Google Scholar 

  27. Moody, G.B., Muldrow, W., Mark, R.G.: A noise stress test for arrhythmia detectors. Comput. Cardiol. 11(3), 381–384 (1984)

    Google Scholar 

  28. Sörnmo, L., Laguna, P.: Bioelectric Signal Processing in Cardiac and Neurological Processing, 1st edn. Academic Press, Cambridge (2005). https://doi.org/10.1016/B978-0-12-437552-9.X5000-4

    Book  Google Scholar 

  29. Yin, X., Xu, Y., Sheng, X., Shen, Y.: Signal denoising method using AIC–SVD and its application to micro-vibration in reaction wheels. Sensors 19(22), 5032 (2019). https://doi.org/10.3390/s19225032

    Article  Google Scholar 

  30. Zhang, X., Tang, J., Zhang, M., Ji, Q.: Noise subspaces subtraction in SVD based on the difference of variance values. J. Vibroeng. 18(7), 4852–4861 (2014). https://doi.org/10.21595/jve.2016.16745

    Article  Google Scholar 

  31. Golyandina, N., Korobeynikov, A.: Basic singular spectrum analysis and forecasting with R. Comput. Stat. Data Anal. 71, 934–954 (2014). https://doi.org/10.1016/j.csda.2013.04.009

    Article  MathSciNet  MATH  Google Scholar 

  32. Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001). https://doi.org/10.1109/51.932724

    Article  Google Scholar 

  33. Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000). https://doi.org/10.1161/01.CIR.101.23.e215

    Article  Google Scholar 

  34. Sharma, R.R., Pachori, R.B.: Baseline wander and power line interference removal from ECG signals using eigenvalue decomposition. Biomed. Signal Process. Control 45, 33–49 (2018). https://doi.org/10.1016/j.bspc.2018.05.002

    Article  Google Scholar 

  35. Al-Zuhairi, D.T., Hameed, A.S., Hameed, I.S.: Secure transceiver based on independent component analysis (ICA) algorithm. Int. J. Intell. Eng. Syst. 14(3), 128–138 (2021). https://doi.org/10.22266/ijies2021.0630.12

    Article  MATH  Google Scholar 

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  1. Department of Electronic, College of Engineering, University of Diyala, Diyala, Iraq

    Dheyaa T. Al-Zuhairi, Abbas Salman Hameed & Isam Salah Hameed

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  1. Dheyaa T. Al-Zuhairi
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  2. Abbas Salman Hameed
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  3. Isam Salah Hameed
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Al-Zuhairi, D.T., Hameed, A.S. & Hameed, I.S. Dual-stage SVD basis approach for ECG signal associated noise removal. SIViP 16, 1489–1496 (2022). https://doi.org/10.1007/s11760-021-02102-1

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