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QRS Detection in ECG Signal with Convolutional Network

  • Pedro Silva
  • Eduardo Luz
  • Elizabeth Wanner
  • David Menotti
  • Gladston MoreiraEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

The QRS complex is a very important part of a heartbeat in the electrocardiogram signal, and it provides useful information for physicians to diagnose heart diseases. Accurately detecting the fiducial points that compose the QRS complex is a challenging task. Another issue concerning the QRS detection is its computational costs since the algorithm should have a fast and real-time response. In this context, there is a trade-off between computational cost and precision. Convolutional networks are a deep learning approach, and it has achieved impressive results in several computer vision and pattern recognition problems. Nowadays there is hardware that fully embeds convolutional network models, significantly reducing computational cost for real-world and real-time applications. In this direction, this work proposes a deep learning approach, based on convolutional network, aiming to detect heartbeat pattern. We tested two different architectures with two different proposes, one very deep and that has small receptive fields, and the other that has larger receptive fields. Preliminary experiments on the MIT-BIH arrhythmia database showed that the studied convolutional network presents promising results for QRS detection which are comparable with state-of-the-art methods.

Keywords

Deep learning Signal process Pattern recognition 

Notes

Acknowledgements

The authors thank UFOP, UFPR and funding Brazilian agencies CNPq, Fapemig and CAPES. We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan X Pascal GPU used for this research.

References

  1. 1.
    Acharya, U.R., Fujita, H., Lih, O.S., Hagiwara, Y., Tan, J.H., Adam, M.: Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf. Sci. 405, 81–90 (2017)CrossRefGoogle Scholar
  2. 2.
    Al Rahhal, M., Bazi, Y., AlHichri, H., Alajlan, N., Melgani, F., Yager, R.: Deep learning approach for active classification of electrocardiogram signals. Inf. Sci. 345, 340–354 (2016)CrossRefGoogle Scholar
  3. 3.
    ANSI/AAMI: Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI), ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008)Google Scholar
  4. 4.
    Chen, S.W., Chen, H.C., Chan, H.L.: A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising. Comput. Methods Programs Biomed. 82(3), 187–195 (2006)CrossRefGoogle Scholar
  5. 5.
    Kohler, B.U., Hennig, C., Orglmeister, R.: The principles of software QRS detection. IEEE Eng. Med. Biol. Mag. 21(1), 42–57 (2002)CrossRefGoogle Scholar
  6. 6.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  7. 7.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  8. 8.
    Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46448-0_2CrossRefGoogle Scholar
  9. 9.
    Luz, E., Moreira, G., Oliveira, L.S., Schwartz, W.R., Menotti, D.: Learning deep off-the-person heart biometrics representations. IEEE Trans. Inf. Forensics Secur. 13, 1258–1270 (2018)CrossRefGoogle Scholar
  10. 10.
    Luz, E.J.S., Schwartz, W.R., Cámara-Chávez, G., Menotti, D.: ECG-based heartbeat classification for arrhythmia detection: a survey. Comput. Methods Programs Biomed. 127, 144–164 (2016)CrossRefGoogle Scholar
  11. 11.
    Nallathambi, G., Principe, J.C.: Integrate and fire pulse train automaton for QRS detection. IEEE Trans. Biomed. Eng. 61(2), 317–326 (2014)CrossRefGoogle Scholar
  12. 12.
    Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. BME–32(3), 230–236 (1985)CrossRefGoogle Scholar
  13. 13.
    Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: British Machine Vision Conference, vol. 1, p. 6 (2015)Google Scholar
  14. 14.
    Poli, R., Cagnoni, S., Valli, G.: Genetic design of optimum linear and nonlinear QRS detectors. IEEE Trans. Biomed. Eng. 42(11), 1137–1141 (1995)CrossRefGoogle Scholar
  15. 15.
    Schons, T., Moreira, G.J.P., Silva, P.H.L., Coelho, V.N., Luz, E.J.S.: Convolutional network for EEG-based biometric. In: Mendoza, M., Velastín, S. (eds.) CIARP 2017. LNCS, vol. 10657, pp. 601–608. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-75193-1_72CrossRefGoogle Scholar
  16. 16.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  17. 17.
    Vedaldi, A., Lenc, K.: MatConvNet: convolutional neural networks for MATLAB. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 689–692. ACM (2015)Google Scholar
  18. 18.
    Zhang, C., Li, P., Sun, G., Guan, Y., Xiao, B., Cong, J.: Optimizing FPGA-based accelerator design for deep convolutional neural networks. In: Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, pp. 161–170. ACM (2015)Google Scholar
  19. 19.
    Zhang, F., Lian, Y.: QRS detection based on multiscale mathematical morphology for wearable ECG devices in body area networks. IEEE Trans. Biomed. Circuits Syst. 3(4), 220–228 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Universidade Federal de Ouro PretoOuro PretoBrazil
  2. 2.Universidade Federal do ParanáCuritibaBrazil
  3. 3.EASAston UniversityBirminghamUK

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