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Detection of the QRS Complexity in Real Time with Bluetooth Communication

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Advances on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC 2020)

Abstract

This paper presents the development of a data acquisition system for the detection of the QRS complexity in an electrocardiogram. The acquisition of the continuous signal has been made with the BITalino biomedical data acquisition card. The signal processing and the graphical user interface has been done on Python programming. Within the interface, the detection of the QRS complex has been performed by implementing the Hilbert transform and the adaptive threshold technique. For the evaluation of the interface, tests have been performed using the obtained signal in real time.

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References

  1. Noro, M., Anzai, D., Wang, J.: Common-mode noise cancellation circuit for wearable ECG. Healthc. Technol. Lett. 4(2), 64–67 (2017)

    Article  Google Scholar 

  2. Alwan, A.: Global Status Reports on Noncommunicable Diseases 2010. World Health Organization, Geneva, Switzerland (2011)

    Google Scholar 

  3. Jain, S.K., Bhaumik, B.: An energy efficient ECG signal processor detecting cardiovascular diseases on smartphone. IEEE Trans. Biomed. Circuits Syst. 11(2), 314–323 (2017)

    Article  Google Scholar 

  4. Wang, X., Zhu, Y., Ha, Y., Qiu, M., Huang, T.: An FPGA-based cloud system for massive ECG data analysis. IEEE Trans. Circuits Syst. II Express Briefs 64(3), 309–313 (2017)

    Article  Google Scholar 

  5. Dey, N., Ashour, A.S., Shi, F., Fong, S.J., Sherratt, R.S.: Developing residential wireless sensor networks for ECG healthcare monitoring. IEEE Trans. Consum. Electron. 63(4), 442–449 (2017)

    Article  Google Scholar 

  6. Lee, S., Park, D., Park, K.H.: QRS complex detection based on primitive. J. Commun. Netw. 19(5), 442–450 (2017)

    Article  Google Scholar 

  7. Sabherwal, P., Agrawal, M., Singh, L.: Automatic detection of the R peaks in single-lead ECG signal. Circuits Syst. Sig. Process. 36(11), 4637–4652 (2017). https://doi.org/10.1007/s00034-017-0537-2

    Article  Google Scholar 

  8. Ning, X., Selesnick, I.W.: ECG enhancement and QRS detection based on sparse derivatives. Biomed. Signal Process. Control 8(6), 713–723 (2013). https://doi.org/10.1016/j.bspc.2013.06.005

    Article  Google Scholar 

  9. Phukpattaranont, P.: QRS detection algorithm based on the quadratic filter. Expert Syst. Appli. 42(2015), 4867–4877 (2015). https://doi.org/10.1016/j.eswa.2015.02.012

    Article  Google Scholar 

  10. Yochuma, M., Renaudb, C., Jacquira, S.: Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT. Biomed. Signal Process. Control 25(2016), 46–52 (2016). https://doi.org/10.1016/j.bspc.2015.10.011

    Article  Google Scholar 

  11. Zhu, H., Dong, J.: An R-peak detection method based on peaks of Shannon energy envelope. Biomed. Signal Process. Control 8(2013), 466–474 (2013). https://doi.org/10.1016/j.bspc.2013.01.001

    Article  Google Scholar 

  12. Jain, S., Ahirwal, M.K., Kumar, A., Bajaj, V.: QRS detection using adaptive filters: a comparative study. ISA Trans. 66(2017), 362–375 (2017). https://doi.org/10.1016/j.isatra.2016.09.023

    Article  Google Scholar 

  13. 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. 13(2), 261–269 (2015)

    Article  Google Scholar 

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Acknowledgements

This project is supported by research grant No. DSA/103.5/16/10473 awarded by PRODEP and by Evangelista Purkyně University. Title of the project - Detection of Cardiac Arrhythmia Patterns through Adaptive Analysis.

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Correspondence to Ricardo Rodríguez-Jorge .

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Rodríguez-Jorge, R., De León-Damas, I., Bila, J. (2021). Detection of the QRS Complexity in Real Time with Bluetooth Communication. In: Barolli, L., Takizawa, M., Yoshihisa, T., Amato, F., Ikeda, M. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2020. Lecture Notes in Networks and Systems, vol 158. Springer, Cham. https://doi.org/10.1007/978-3-030-61105-7_43

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  • DOI: https://doi.org/10.1007/978-3-030-61105-7_43

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  • Print ISBN: 978-3-030-61104-0

  • Online ISBN: 978-3-030-61105-7

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