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Correlation-based classification of heartbeats for individual identification

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Abstract

This paper proposes new techniques to delineate P and T waves efficiently from heartbeats. The delineation results have been found to be optimum and stable in comparison to other published results. These delineators are used along with QRS complex to extract various features of classes time interval, amplitude and angle from clinically dominant fiducials on each heartbeat of the electrocardiogram (ECG). A new identification system has been proposed in this study, which uses these features and makes the decision on the identity of an individual with respect to a given database. The system has been tested against a set of 250 ECG recordings prepared from 50 individuals of Physionet. The matching decisions are made on the basis of correlation between heartbeat features among individuals. The proposed system has achieved an equal error rate of less than 1.01 with an accuracy of 99%.

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References

  • Bazett HC (1920) An analysis of the time-relations of electrocardiograms. Heart 7:353–370

    Google Scholar 

  • Biel L, Pettersson O, Lennart P, Peter W (2001) ECG analysis: a new approach in human identification. IEEE Trans Instrum Meas 50(3):808–812

    Article  Google Scholar 

  • David FD (2005) The normal ECG in childhood and adolescence. Heart 91:1626–1630

    Article  Google Scholar 

  • Friesen GM, Thomas CJ, Manal AJ, Stanford LY, Stephen RQ, Troy N (1990) A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Trans Biomed Eng 37(1):85–98

    Article  Google Scholar 

  • Irvine JM, Israel SA, Scruggs WT, Worek WJ (2008) eigenPulse: robust human identification from cardiovascular function. Pattern Recognit 41:3427–3435

    Article  Google Scholar 

  • Israel SA, Irvine JM, Andrew C, Mark DW, Brenda KW (2005) ECG to identify individuals. Pattern Recognit 38(1):133–142

    Article  Google Scholar 

  • Kligfield P (2002) The centennial of the Einthoven electrocardiogram. J Electrocardiol 35:123–129

    Article  Google Scholar 

  • Laguna P, Mark RG, Goldberger A, Moody GB (1997) A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Comput Cardiol IEEE Comput Soc Press 24:673–676

    Google Scholar 

  • Li C, Zheng C, Tai C (1995) Detection of ECG characteristics points using wavelet transforms. IEEE Trans Biomed Eng 42(1):21–28

    Article  Google Scholar 

  • Martinez JP, Almeida R, Olmos S, Rocha AP, Laguna P (2004) A wavelet-based ECG delineator: evaluation on standard database. IEEE Trans Biomed Eng 51(4):570–581

    Article  Google Scholar 

  • Menard A (1981) Dual microprocessor system for cardiovascular data acquisition, processing and recordings. In: Proceedings 1981 IEEE Int Conf Industrial Elect Contr Instrument, pp 64–69

  • Pan J, Tompkins WJ (1985) A real time QRS detection algorithm. IEEE Trans Biomed Eng 32(3):230–236

    Article  Google Scholar 

  • Rijnbeek PR, Witsenburg M, Schrama E, Hess J, Kors JA (2001) New normal limits for the pediatric electrocardiogram. Eur Heart J 22:702–711

    Article  Google Scholar 

  • Saladin KS (2007) Anatomy and physiology: the unity of form and function, 4th edn. WCB McGraw-Hill, Boston

  • Salim G, Boucher JM (2005) Hidden Markov tree model applied to ECG delineation. IEEE Trans Instrum Meas 54(6):2163–2168

    Article  Google Scholar 

  • Shen TW, Tompkins WJ, Hu YH (2002) One-lead ECG for identity verification. In: Proceedings second joint EMBS/BMES conference, pp 62–63

  • Sun Y, Chan KL, Krishnan SM (2005) Characteristic wave detection in ECG signal using morphological transform. BMC Cardiovasc Disord 28:1417–2261

    Google Scholar 

  • Taneja T, Mahnert BW, Passman R, Goldberger J, Kadish A (2001) Effects of sex and age on electrocardiographic and cardiac electrophysiological properties in adults. Pacing Clin Electrophysiol 24(1):16–21

    Article  Google Scholar 

  • The CSE Working Party (1985) Recommendations for measurement standards in quantitative electrocardiography. Eur Heart J 6:815–825

    Google Scholar 

  • Wang Y, Agrafioti F, Hatzinakos D, Plataniotis KN (2008) Analysis of human electrocardiogram for biometric recognition. EURASIP J Adv Signal Process vol 2008, 11 pp. doi:10.1155/2008/148658

  • Yanushkevich SN (2006) Synthetic biometrics: a survey. In: Proceedings of international joint conference on neural networks (IJCNN), pp 676–683

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Institute of Engineering and Technology, UP Technical University Lucknow, Lucknow, 226021, India

    Yogendra Narain Singh

  2. Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, India

    Phalguni Gupta

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  1. Yogendra Narain Singh
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  2. Phalguni Gupta
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Correspondence to Yogendra Narain Singh.

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Singh, Y.N., Gupta, P. Correlation-based classification of heartbeats for individual identification. Soft Comput 15, 449–460 (2011). https://doi.org/10.1007/s00500-009-0525-y

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  • DOI: https://doi.org/10.1007/s00500-009-0525-y

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