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Shifted 1D-LBP Based ECG Recognition System

  • Meryem Regouid
  • Mohamed Benouis
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 64)

Abstract

ECG analysis has been investigated as promising biometric in many fields especially in medical science and cardiovascular disease for last decades in order to exploit the discriminative capability provided by these liveness measures developing a robust ECG based recognition system. In this paper, an ECG biometric recognition system was proposed based on shifted 1D-LBP. Shifted 1D-LBP was applied to extract the representative non-fiducial features from preprocessed and segmented ECG heartbeats. For matching step, K Nearest Neighbors (KNN) was adopted. Two benchmark databases namely MIT-BIH/Normal Sinus Rhythm and ECG-ID database were used to validate the proposed approach. A Correct Recognition Rate (CRR) of 100% and 97% was achieved with MIT-BIH/Normal Sinus Rhythm and ECG-ID databases, respectively.

Keywords

ECG Shifted 1D-LBP KNN MIT-BIH/normal sinus rhythm ECG-ID CRR 

References

  1. 1.
    Woodward, J.D., Webb, K.W., Newton, E.M., Bradley, M.A., Rubenson, D.: Army Biometric Applications: Identifying and Addressing Sociocultural Concerns. Rand Corporation (2001)Google Scholar
  2. 2.
    Jain, A.K., Flynn, P., Ross, A.A.: Handbook of Biometrics. Springer Science \& Business Media, New York (2007)Google Scholar
  3. 3.
    Biel, L., Pettersson, O., Philipson, L., Wide, P.: ECG analysis: a new approach in human identification. IEEE Trans. Instrum. Meas. 50(3), 808–812 (2001)CrossRefGoogle Scholar
  4. 4.
    Shen, T.-W., Tompkins, W. J., Hu, Y.H.: One-lead ECG for identity verification. In: Engineering in medicine and biology, 2002. Proceedings of the Second Joint 24th Annual Conference and the Annual Fallmeeting of the Biomedical Engineering Society Embs/Bmes Conference, vol. 1, pp. 62–63 (2002)Google Scholar
  5. 5.
    Belgacem, N., Nait-ali, A., Fournier, R., Bereksi Reguig, F.: ECG based human identification using random forests. In: The International Conference on E-Technologies and Business on the Web (EBW2013), Bangkok, Thailand (2013)Google Scholar
  6. 6.
    Wübbeler, G., Stavridis, M., Kreiseler, D., Bousseljot, R.-D., Elster, C.: Verification of humans using the electrocardiogram. Pattern Recogn. Lett. 28(10), 1172–1175 (2007)CrossRefGoogle Scholar
  7. 7.
    Islam, M.S., Alajlan, N.: Biometric template extraction from a heartbeat signal captured from fingers. Multimed. Tools Appl. 76(10), 12709–12733 (2017)CrossRefGoogle Scholar
  8. 8.
    Louis, W., Hatzinakos, D., Venetsanopoulos, A.: One dimensional multi-resolution local binary patterns features (1DMRLBP) for regular electrocardiogram (ECG) waveform detection. In: 2014 19th International Conference on Digital Signal Processing (DSP), pp. 601–606 (2014)Google Scholar
  9. 9.
    Fratini, A., Sansone, M., Bifulco, P., Cesarelli, M.: Individual identification via electrocardiogram analysis. BioMedical Eng. Online 14(1) (2015)Google Scholar
  10. 10.
    Pereira Coutinho, D., Figueiredo, M., Fred, A., Gamboa, H., Silva, H.: Novel fiducial and non-fiducial approaches to electrocardiogram-based biometric systems. IET Biom. 2(2), 64–75 (2013)CrossRefGoogle Scholar
  11. 11.
    Dar, M.N., Akram, M.U., Shaukat, A., Khan, M.A.: ECG based biometric identification for population with normal and cardiac anomalies using hybrid HRV and DWT features. In: 2015 5th International Conference on IT Convergence and Security (ICITCS), pp. 1–5 (2015)Google Scholar
  12. 12.
    Chun, S.Y.: Single pulse ECG-based small scale user authentication using guided filtering. In: 2016 International Conference on Biometrics (ICB), pp. 1–7Google Scholar
  13. 13.
    Barra, S., Casanova, A., Fraschini, M., Nappi, M.: Fusion of physiological measures for multimodal biometric systems. Multimed. Tools Appl. 76(4), 4835–4847 (2017)CrossRefGoogle Scholar
  14. 14.
    Bassiouni, M.M., El-Dahshan, E.-S.A., Khalefa, W., Salem, A.M.: Intelligent hybrid approaches for human ECG signals identification. SIViP 12(5), 941–949 (2018)CrossRefGoogle Scholar
  15. 15.
    Ojala, T., Pietikäinen, M.: Unsupervised texture segmentation using feature distributions. Pattern Recogn. 32(3), 477–486 (1999)CrossRefGoogle Scholar
  16. 16.
    He, S., Soraghan, J.J., O’Reilly, B.F., Xing, D.: Quantitative analysis of facial paralysis using local binary patterns in biomedical videos. IEEE Trans. Biomed. Eng. 56(7), 1864–1870 (2009)CrossRefGoogle Scholar
  17. 17.
    Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns, vol. 40. Springer Science \& Business Media, London (2011)Google Scholar
  18. 18.
    Chatlani, N., Soraghan, J.J.: Local binary patterns for 1-D signal processing. In: 2010 18th European Signal Processing Conference, pp. 95–99 (2010)Google Scholar
  19. 19.
    Boodoo-Jahangeer, N.B., Baichoo, S.: LBP-based ear recognition. In: 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 1–4 (2013)Google Scholar
  20. 20.
    Ertuğrul, F., Kaya, Y., Tekin, R., Almali, M.N.: Detection of Parkinson’s disease by shifted one dimensional local binary patterns from gait. Expert. Syst. Appl. 56, 156–163 (2016)CrossRefGoogle Scholar
  21. 21.
    Goldberger, A., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000). Circulation Electronic Pages. http://circ.ahajournals.org/cgi/content/full/101/23/e215. Accessed 13 June 2000
  22. 22.
    Nemirko, A.P., Lugovaya, T.S.: Biometric human identification based on electrocardiogram. In: Proceedings of the 8th Russian Conference on Mathematical Methods of Pattern Recognition, Moscow, Russian, pp. 20–26 (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of Ferhat AbbasSetifAlgeria
  2. 2.Computer Science DepartmentUniversity of M’sila BPM’silaAlgeria

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