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Linear phase properties of the singular spectrum analysis components for the estimations of the RR intervals of electrocardiograms

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Abstract

Denoising is the first step in both the QRS complex detection and the beat classification. However, infinite impulse response filters usually exhibit nonlinear phase responses. As a result, the group delays of the output signals based on the infinite impulse response filtering are different at different time instants. This causes the inaccuracies of the estimations of the RR intervals of the electrocardiograms. In this paper, the denoising is performed based on the singular spectrum analysis approach. The linear phase properties of the singular spectrum analysis components are investigated. Finally, the RR intervals of the denoised electrocardiograms are estimated and those obtained based on the infinite impulse response filtering approach are compared. The computer numerical simulation results show that the estimation performance based on the singular spectrum analysis approach outperforms that based on the infinite impulse response filtering approach.

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References

  1. Alagoz, B.B., Yeroglu, C., Kavuran, G., Ates, A.: Detection of RR interval alterations in ECG signals by using first order fractional filter. In: 39th International Conference on Telecommunications and Signal Processing, pp. 579–582. IEEE (2016)

  2. Karamchandani, S.H., Desai, U.B., Merchant, S.N., Jindal, G.D.: Parallel support vector architectures for taxonomy of radial pulse morphology. Signal Image Video Process. 7(5), 975–990 (2013)

    Article  Google Scholar 

  3. Allami, R., Stranieri, A., Balasubramanian, V., Jelinek, H.F.: A count data model for heart rate variability forecasting and premature ventricular contraction detection. Signal Image Video Process. 11(8), 1427–1435 (2017)

    Article  Google Scholar 

  4. Cnockaert, L., Migeotte, P.F., Daubigny, L., Prisk, G.K., Grenez, F., Sá, R.C.: A method for the analysis of respiratory sinus arrhythmia using continuous wavelet transforms. IEEE Trans. Biomed. Eng. 55(5), 1640–1642 (2008)

    Article  Google Scholar 

  5. Leite, J.P.R., Moreno, R.L.: Heartbeat classification with low computational cost using Hjorth parameters. IET Signal Process. 12(4), 431–438 (2017)

    Article  Google Scholar 

  6. Tekeste, T., Saleh, H., Mohammad, B., Khandoker, A., Jelinek, H., Ismail, M.: A nanowatt real-time cardiac autonomic neuropathy detector. IEEE Trans. Biomed. Circuits Syst. 12(4), 739–750 (2018)

    Article  Google Scholar 

  7. Chen, S.W.: A wavelet-based heart rate variability analysis for the study of nonsustained ventricular tachycardia. IEEE Trans. Biomed. Eng. 49(7), 736–742 (2002)

    Article  Google Scholar 

  8. Jadvar, H., Jenkins, J.M., Stewart, R.E., Schwaiger, M., Arzbaecher, R.C.: Computer analysis of the electrocardiogram during esophageal pacing cardiac stress. IEEE Trans. Biomed. Eng. 38(11), 1089–1099 (1991)

    Article  Google Scholar 

  9. Melillo, P., De Luca, N., Bracale, M., Pecchia, L.: Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability. IEEE J. Biomed. Health Inform. 17(3), 727–733 (2013)

    Article  Google Scholar 

  10. Li, Y., Zhang, Y., Zhao, L., Zhang, Y., Liu, C., Zhang, L., Zhang, L., Li, Z., Wang, B., Ng, E., Li, J., He, Z.: Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure. IEEE Access 6, 39734–39744 (2018)

    Article  Google Scholar 

  11. Valenza, G., Wendt, H., Kiyono, K., Hayano, J., Watanabe, E., Yamamoto, Y., Abry, P., Barbieri, R.: Mortality prediction in severe congestive heart failure patients with multifractal point-process modeling of heartbeat dynamics. IEEE Trans. Biomed. Eng. 65(10), 2345–2354 (2018)

    Article  Google Scholar 

  12. Suman, S., Kumar, A., Singh, G.K.: A new method for higher-order linear phase FIR digital filter using shifted Chebyshev polynomials. Signal Image Video Process. 10(6), 1041–1048 (2016)

    Article  Google Scholar 

  13. Shin, J.H., Lee, K.M., Park, K.S.: Non-constrained monitoring of systolic blood pressure on a weighing scale. Physiol. Meas. 30(7), 679–693 (2009)

    Article  Google Scholar 

  14. Aydın, S., Saraoǧlu, H.M., Kara, S.: Singular spectrum analysis of sleep EEG in insomnia. J. Med. Syst. 35(4), 457–461 (2011)

    Article  Google Scholar 

  15. Mineva, A., Popivanov, D.: Method for single-trial readiness potential identification, based on singular spectrum analysis. J. Neurosci. Methods 68(1), 91–99 (1996)

    Article  Google Scholar 

  16. Romero, F., Alonso, F.J., Cubero, J., Galán-Marín, G.: An automatic SSA-based de-noising and smoothing technique for surface electromyography signals. Biomed. Signal Process. Control 18, 317–324 (2015)

    Article  Google Scholar 

  17. Golyandina, N., Nekrutkin, V., Zhigljavsky, A.A.: Analysis of Time Series Structure: SSA and Related Techniques. Chapman and Hall/CRC, London (2001)

    Book  Google Scholar 

  18. Zabalza, J., Ren, J., Wang, Z., Zhao, H., Wang, J., Marshall, S.: Fast implementation of singular spectrum analysis for effective feature extraction in hyperspectral imaging. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 2845–2853 (2014)

    Article  Google Scholar 

  19. Wu, Z.W., Yao, M.L., Ma, H.G., Jia, W.M.: De-noising MEMS inertial sensors for low-cost vehicular attitude estimation based on singular spectrum analysis and independent component analysis. Electron. Lett. 49(14), 892–893 (2013)

    Article  Google Scholar 

  20. Hofmanis, J., Caspary, O., Louis-Dorr, V., Ranta, R., Maillard, L.: Denoising depth EEG signals during DBS using filtering and subspace decomposition. IEEE Trans. Biomed. Eng. 60(10), 2686–2695 (2013)

    Article  Google Scholar 

  21. Cantoni, A., Butler, P.: Eigenvalues and eigenvectors of symmetric centrosymmetric matrices. Linear Algebra Appl. 13(3), 275–288 (1976)

    Article  MathSciNet  Google Scholar 

  22. Cantoni, A., Butler, P.: Properties of the eigenvectors of persymmetric matrices with applications to communication theory. IEEE Trans. Commun. 24(8), 804–809 (1976)

    Article  MathSciNet  Google Scholar 

  23. Makhoul, J.: On the eigenvectors of symmetric Toeplitz matrices. IEEE Trans. Acoust. Speech Signal Process. 29(4), 868–872 (1981)

    Article  MathSciNet  Google Scholar 

  24. The MIT-BIH Arrhythmia database. http://www.physionet.org/physiobank/database/mitdb/. Accessed 1 Mar 2019

  25. Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32(3), 230–236 (1985)

    Article  Google Scholar 

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Acknowledgements

This paper was supported partly by the National Nature Science Foundation of China (Nos. U1701266, 61372173 and 61671163), the Team Project of the Education Ministry of the Guangdong Province (No. 2017KCXTD011), the Guangdong Higher Education Engineering Technology Research Center for Big Data on Manufacturing Knowledge Patent (No. 501130144) and Hong Kong Innovation and Technology Commission, Enterprise Support Scheme (No. S/E/070/17).

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  1. Faculty of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China

    Xiaozhu Mo, Bingo Wing-Kuen Ling, Qiuliang Ye & Yang Zhou

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  1. Xiaozhu Mo
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  2. Bingo Wing-Kuen Ling
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Correspondence to Bingo Wing-Kuen Ling.

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Mo, X., Ling, B.WK., Ye, Q. et al. Linear phase properties of the singular spectrum analysis components for the estimations of the RR intervals of electrocardiograms. SIViP 14, 325–332 (2020). https://doi.org/10.1007/s11760-019-01560-y

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