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.
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References
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)
Jain, A.K., Flynn, P., Ross, A.A.: Handbook of Biometrics. Springer Science & Business Media, New York (2007)
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)
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)
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)
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)
Islam, M.S., Alajlan, N.: Biometric template extraction from a heartbeat signal captured from fingers. Multimed. Tools Appl. 76(10), 12709–12733 (2017)
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)
Fratini, A., Sansone, M., Bifulco, P., Cesarelli, M.: Individual identification via electrocardiogram analysis. BioMedical Eng. Online 14(1) (2015)
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)
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)
Chun, S.Y.: Single pulse ECG-based small scale user authentication using guided filtering. In: 2016 International Conference on Biometrics (ICB), pp. 1–7
Barra, S., Casanova, A., Fraschini, M., Nappi, M.: Fusion of physiological measures for multimodal biometric systems. Multimed. Tools Appl. 76(4), 4835–4847 (2017)
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)
Ojala, T., Pietikäinen, M.: Unsupervised texture segmentation using feature distributions. Pattern Recogn. 32(3), 477–486 (1999)
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)
Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns, vol. 40. Springer Science & Business Media, London (2011)
Chatlani, N., Soraghan, J.J.: Local binary patterns for 1-D signal processing. In: 2010 18th European Signal Processing Conference, pp. 95–99 (2010)
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)
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)
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
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)
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Regouid, M., Benouis, M. (2019). Shifted 1D-LBP Based ECG Recognition System. In: Chikhi, S., Amine, A., Chaoui, A., Saidouni, D.E. (eds) Modelling and Implementation of Complex Systems. MISC 2018. Lecture Notes in Networks and Systems, vol 64. Springer, Cham. https://doi.org/10.1007/978-3-030-05481-6_13
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DOI: https://doi.org/10.1007/978-3-030-05481-6_13
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