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Adaptive Threshold, Wavelet and Hilbert Transform for QRS Detection in Electrocardiogram Signals

Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT,volume 13)

Abstract

This paper combines Hilbert and Wavelet transforms and an adaptive threshold technique to detect the QRS complex of electrocardiogram signals. The method is performed in a window framework. First, the Wavelet transform is applied to the ECG signal to remove noise. Next, the Hilbert transform is applied to detect dominant peak points in the signal. Finally, the adaptive threshold technique is applied to detect R-peaks, Q, and S points. The performance of the algorithm is evaluated against the MIT-BIH arrhythmia database, and the numerical results indicated significant detection accuracy.

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  • DOI: 10.1007/978-3-319-69835-9_73
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References

  1. World Health Organization (WHO), The Top 10 Causes of Death (2014). http://www.who.int/mediacentre/factsheets/fs310/en/. Accessed Feb 2016

  2. Elgendi, M., Eskofier, B., Dokos, S., Abbott, D.: Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems. PLoS ONE 9(1), e84018 (2014)

    CrossRef  Google Scholar 

  3. Martinez Molina, A., Rodriguez Jorge, R., Villa-Angulo, R., Bila, J., Mizera-Pietraszko, J., Torres Arguelles, S.: Review on higher-order neural units to monitor cardiac arrhythmia patterns. In: Advances in Digital Technologies: Proceedings of the 8th International Conference on Applications of Digital Information and Web Technologies 2017, vol. 295, pp. 219–231. IOS Press (2017)

    Google Scholar 

  4. Herrera, J.E.C., Rodriguez Jorge, R., Vergara Villegas, O.O., Cruz Sánchez, V.G., Bila, J., Nandayapa Alfaro, M. de J., Ponce, I.U., Soto Marrufo, A.I.: Monitoring of cardiac arrhythmia patterns by adaptive analysis. In: Xhafa, F., Barolli, L., Amato, F. (eds.) Advances on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC, Lecture Notes on Data Engineering and Communications Technologies, vol. 1. Springer, Cham (2016)

    Google Scholar 

  5. Elgendi, M.: Fast QRS detection with and optimized knowledge-based method: evaluation on 11 standard ECG databases. PLoS ONE 8(9), e73557 (2013)

    CrossRef  Google Scholar 

  6. Lewandowski, J., Arochena, R.N.H., Chao, K.-M.: A simple real-time QRS detection algorithm utilizing curve-length concept with combined adaptive threshold for electrocardiogram signal classification. In: TENCON – 2012 IEEE Region 10 Conference, pp. 1–6 (2012)

    Google Scholar 

  7. Noh, Y., Jeong, D.: Implementation of a data packet generator using pattern matching for wearable ecg monitoring systems. Sensors 14(7), 12623–31269 (2014)

    CrossRef  Google Scholar 

  8. Jeon, T., Kim, B., Jeon, M., Lee, B.: Implementation of a portable device for real-time ecg signal analysis. Biomed. Eng. Online 13(160), 1–13 (2014)

    Google Scholar 

  9. Dohare, A., Kumar, V., Kumar, R.: An efficient new method for the detection of qrs in electrocardiogram. Comput. Electr. Eng. 40(5), 1717–1730 (2014)

    CrossRef  Google Scholar 

  10. Pal, S., Mitra, M.: Empirical mode decomposition based ecg enhancement and QRS detection. Comput. Biol. Med. 42(1), 83–92 (2012)

    CrossRef  Google Scholar 

  11. Manikandan, M., Soman, K.: A novel method for detecting R-peaks in electrocardiogram (ECG). Biomed. Signal Process. Control 7(2), 118–128 (2012)

    CrossRef  Google Scholar 

  12. Ning, X., Selesnick, I.: ECG enhancement and QRS detection based on sparse derivatives. Biomed. Signal Process. Control 8(6), 713–723 (2013)

    CrossRef  Google Scholar 

  13. Zhang, F., Lian, Y.: Qrs detection based on morphological filter and energy envelope for applications in body sensor networks. J. Signal Process. Sys. 64(2), 187–194 (2011)

    CrossRef  Google Scholar 

  14. Li, H., Wang, X.: Detection of electrocardiogram characteristics points using lifting wavelet transform and hilbert transform. T. I. Meas. Control 35(5), 574–582 (2013)

    CrossRef  Google Scholar 

  15. Zidelmal, Z., Amirou, A., Ould-Abdeslam, D., Moukadem, A.: QRS detection using s-transform and Shannon energy. Comput. Meth. Prog. Bio. 116(1), 1–9 (2014)

    CrossRef  Google Scholar 

  16. Arbateni, K., Bennia, A.: Sigmoidal radial basis function ANN for QRS complex detection. Neurocomputing 145, 438–450 (2014)

    CrossRef  Google Scholar 

  17. Rodriguez, R., Mexicano, A., Bila, J., Ponce, R., Cervantes, S., Martinez, A.: Hilbert transform and neural networks for identification and modeling of ECG complex. In: 2013 Third International Conference on Innovative Computing Technology (INTECH), pp. 327–332. IEEE (2013)

    Google Scholar 

  18. Rodríguez, R., Bila, J., Mexicano, A., Cervantes, S., Ponce, R., Nghien, N.B.: Hilbert-Huang transform and neural networks for electrocardiogram modeling and prediction. In: 2014 10th International Conference on Natural Computation (ICNC), pp. 561–567. IEEE (2014)

    Google Scholar 

  19. Rodríguez, R., Mexicano, A., Bila, J., Cervantes, S., Ponce, R.: Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis. J. Appl. Res. Technol. 2(13), 261–269 (2015)

    CrossRef  Google Scholar 

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Acknowledgments

This project is supported by Research Grant No. DSA/103.5/16/10473 awarded by PRODEP and the Autonomous University of Ciudad Juarez. Title - Detection of Cardiac Arrhythmia Patterns through Adaptive Analysis.

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Correspondence to Ricardo Rodriguez Jorge .

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Rodriguez Jorge, R., García, E.M., Córdoba, R.T., Bila, J., Mizera-Pietraszko, J. (2018). Adaptive Threshold, Wavelet and Hilbert Transform for QRS Detection in Electrocardiogram Signals. In: Xhafa, F., Caballé, S., Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-69835-9_73

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  • DOI: https://doi.org/10.1007/978-3-319-69835-9_73

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  • Online ISBN: 978-3-319-69835-9

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