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The New Approach for ECG Signal Quality Index Estimation on the Base of Robust Statistic

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Information Technology in Biomedicine (ITIB 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1011))

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Abstract

In this article a new method for automatic determination of the quality indicator of electrocardiographic signal (ECG) is presented. The proposed method allows to determine the time intervals in which the ECG signal is of such quality that it is possible to detect the R-waves of the electrocardiogram. The developed method is based on analysis of the median standard deviation (MAD). The method is divided into three stages: determination of variability of MAD, finding of the time intervals in which the signal is in saturation and the decision stage, on the basis of which the masking signal is created. The performance of the proposed method has been tested with using the ECG recordings taken from the MIT-BIH Noise Stress Test database and the telehealth database. The obtained results show the usefulness in location of artifacts in the ECG signal. The proposed algorithm can be useful especially in acquisition of electrophysiological signals for mobile devices for telemedicine purposes.

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References

  1. Alfonso, V., Tompkins, W., Nguyen, T., Michler, K., Luo, S.: Comparing stress ECG enhancement algorithms. IEEE Eng. Med. Biol. 5, 37–44 (1996)

    Article  Google Scholar 

  2. Behar, J., Oster, J., Li, Q., Clifford, G.: A single channel ECG quality metric. Comput. Cardiol. 38, 381–384 (2012)

    Google Scholar 

  3. Behar, J., Oster, J., Li, Q., Clifford, G.: ECG signal quality during arrhythmia and its application to false alarm reduction. IEEE Trans. Biomed. Eng. 60(6), 1660–1666 (2013). https://doi.org/10.1109/TBME.2013.2240452

    Article  Google Scholar 

  4. Berkaya, S., Uysal, A., Gunal, E., Ergin, S., Gunal, S., Gulmezoglu, M.: A survey on ECG analysis. Biomed. Signal Process. Control 43, 216–235 (2018). https://doi.org/10.1016/j.bspc.2018.03.003

    Article  Google Scholar 

  5. Clifford, G.D., Behar, J., Li, Q., Rezek, I.: Signal quality and data fusion for determining the clinical acceptability of electrocardiograms. Physiol. Meas. 33 1419–1433 (2012). https://doi.org/10.1088/0967-3334/33/9/1419

  6. Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101, e215–20 (2000)

    Google Scholar 

  7. Gutiérrez-Rivas, R., García, J., Marnane, W., Hernández, A.: Novel real-time low-complexity QRS complex detector based on adaptive thresholding. IEEE Sens. J. 15(10), 6036–6043 (2015). https://doi.org/10.1109/JSEN.2015.2450773

    Article  Google Scholar 

  8. Johannesen, L., Galeotti, L.: Automatic ECG quality scoring methodology: mimicking human annotators. Physiol. Meas. 33(9), 1479–89 (2012). http://stacks.iop.org/0967-3334/33/i=9/a=1479

  9. Khamis, H., Weiss, R., Xie, Y., Chang, C.W., Lovell, N.H., Redmond, S.J.: QRS detection algorithm for telehealth electrocardiogram recordings. IEEE Trans. Biomed. Eng. 63, 1377–1388 (2016). https://doi.org/10.1109/TBME.2016.2549060

    Article  Google Scholar 

  10. Khamis, H., Weiss, R., Xie, Y., Chang, C.W., Lovell, N.H., Redmond, S.J.: Tele ECG database: 250 telehealth ECG records (collected using dry metal electrodes) with annotated QRS and artifact masks, and matlab code for the unsw artifact detection and UNSW QRS detection algorithms. Harv. Dataverse (2016). https://doi.org/10.7910/DVN/QTG0EP

    Article  Google Scholar 

  11. Łęski, J.: Robust weighted averaging. IEEE Trans. Biomed. Eng. 49(8), 796–804 (2002). https://doi.org/10.1109/TBME.2002.800757

    Article  Google Scholar 

  12. Lee, J., McManus, D.D., Merchant, S., Chon, K.H.: Automatic motion and noise artifact detection in holter ECG data using empirical mode decomposition and statistical approaches. IEEE Trans. Biomed. Eng. 59, 1499–1506 (2012). https://doi.org/10.1109/TBME.2011.2175729

    Article  Google Scholar 

  13. Li, Q., Rajagopalan, C., Clifford, G.D.: A machine learning approach to multi-level ECG signal quality classification. Comput. Methods Programs Biomed. 117(3), 435–47 (2014). https://doi.org/10.1016/j.cmpb.2014.09.002

    Article  Google Scholar 

  14. Manikandan, M.S., Soman, K.P.: A novel method for detecting r-peaks in electrocardiogram (ECG) signal. Biomed. Signal Process. Control 7, 118–128 (2012). https://doi.org/10.1016/j.bspc.2011.03.004

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  17. Morgado, E., Alonso-Atienza, F., Santiago-Mozos, R., Barquero-Pérez, Ó., Silva, I., Ramos, J., Mark, R.G.: Quality estimation of the electrocardiogram using cross-correlation among leads. In: Biomedical Engineering Online (2015)

    Google Scholar 

  18. Nangalia, V., Prytherch, D.R., Smith, G.B.: Health technology assessment review: Remote monitoring of vital signs-current status and future challenges. Crit. Care 14, 233 (2010). https://doi.org/10.1186/cc9208

    Article  Google Scholar 

  19. Orphanidou, C., Bonnici, T., Charlton, P., Clifton, D.A., Vallance, D., Tarassenko, L.: Signal quality indices for the electrocardiogram and photoplethysmogram: derivation and applications to wireless monitoring. IEEE J. Biomed. Health Inf. 19, 832–838 (2015)

    Google Scholar 

  20. Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 3, 230–236 (1985). https://doi.org/10.1109/TBME.1985.325532

    Article  Google Scholar 

  21. Redmond, S., Lovell, N., Basilakis, J., Celler, B.: ECG quality measures in telecare monitoring. In: Conference Proceedings IEEE Engineering in Medicine and Biology Society, pp. 2869–2872 (2008)

    Google Scholar 

  22. Redmond, S.J., Xie, Y., Chang, D., Basilakis, J., Lovell, N.H.: Electrocardiogram signal quality measures for unsupervised telehealth environments. Physiol. Meas. 33(9), 1517 (2012). https://doi.org/10.1088/0967-3334/33/9/1517

    Article  Google Scholar 

  23. Satija, U., Ramkumar, B., Manikandan, M.S.: A review of signal processing techniques for electrocardiogram signal quality assessment. IEEE Rev. Biomed. Eng. 11, 36–52 (2018). https://doi.org/10.1109/RBME.2018.2810957

    Article  Google Scholar 

  24. Satija, U., Ramkumar, B., Manikandan, M.S.: An automated ECG signal quality assessment method for unsupervised diagnostic systems. Biocybern. Biomed. Eng. 38(1), 54–70 (2018). https://doi.org/10.1016/j.bbe.2017.10.002

    Article  Google Scholar 

  25. Shao, M., Nikias, C.: Signal processing with fractional lower order moments: stable processes and their applications. Proc. IEEE 81(7), 986–1010 (1993). https://doi.org/10.1109/5.231338

    Article  Google Scholar 

  26. Sörnmo, L., Laguna, P.: Electrocardiogram (ECG) signal processing. In: Wiley Encyclopedia of Biomedical Engineering. John Wiley and Sons, Inc. (2006). https://doi.org/10.1002/9780471740360.ebs1482

  27. Zhao, Z., Zhang, Y.: SQI quality evaluation mechanism of single-lead ECG signal based on simple heuristic fusion and fuzzy comprehensive evaluation. Front. Physiol. 9, (2018). https://doi.org/10.3389/fphys.2018.00727

  28. Zareba, W., Maison-Blanche, P., Locati, E.H.: Noninvasive Electrocardiology in Clinical Practice. Futura Publishing Co., New York (2001)

    Google Scholar 

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Acknowledgment

This research was supported by statutory funds of the Institute of Electronics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology.

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Authors and Affiliations

  1. Faculty of Automatic Control, Electronics and Computer Science, Institute of Electronics, Silesian University of Technology, Akademicka St. 16, 44-100, Gliwice, Poland

    Tomasz Pander & Tomasz Przybyła

Authors
  1. Tomasz Pander
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  2. Tomasz Przybyła
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Corresponding author

Correspondence to Tomasz Pander .

Editor information

Editors and Affiliations

  1. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Ewa Pietka

  2. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Pawel Badura

  3. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Jacek Kawa

  4. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Wojciech Wieclawek

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Pander, T., Przybyła, T. (2019). The New Approach for ECG Signal Quality Index Estimation on the Base of Robust Statistic. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2019. Advances in Intelligent Systems and Computing, vol 1011. Springer, Cham. https://doi.org/10.1007/978-3-030-23762-2_43

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