Soft Computing

, Volume 15, Issue 3, pp 449–460 | Cite as

Correlation-based classification of heartbeats for individual identification

Focus

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%.

Keywords

Heartbeat Biometrics Individual identification Correlation 

References

  1. Bazett HC (1920) An analysis of the time-relations of electrocardiograms. Heart 7:353–370Google Scholar
  2. Biel L, Pettersson O, Lennart P, Peter W (2001) ECG analysis: a new approach in human identification. IEEE Trans Instrum Meas 50(3):808–812CrossRefGoogle Scholar
  3. David FD (2005) The normal ECG in childhood and adolescence. Heart 91:1626–1630CrossRefGoogle Scholar
  4. 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):85–98CrossRefGoogle Scholar
  5. Irvine JM, Israel SA, Scruggs WT, Worek WJ (2008) eigenPulse: robust human identification from cardiovascular function. Pattern Recognit 41:3427–3435CrossRefGoogle Scholar
  6. Israel SA, Irvine JM, Andrew C, Mark DW, Brenda KW (2005) ECG to identify individuals. Pattern Recognit 38(1):133–142CrossRefGoogle Scholar
  7. Kligfield P (2002) The centennial of the Einthoven electrocardiogram. J Electrocardiol 35:123–129CrossRefGoogle Scholar
  8. 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 24:673–676Google Scholar
  9. Li C, Zheng C, Tai C (1995) Detection of ECG characteristics points using wavelet transforms. IEEE Trans Biomed Eng 42(1):21–28CrossRefGoogle Scholar
  10. 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):570–581CrossRefGoogle Scholar
  11. Menard A (1981) Dual microprocessor system for cardiovascular data acquisition, processing and recordings. In: Proceedings 1981 IEEE Int Conf Industrial Elect Contr Instrument, pp 64–69Google Scholar
  12. Pan J, Tompkins WJ (1985) A real time QRS detection algorithm. IEEE Trans Biomed Eng 32(3):230–236CrossRefGoogle Scholar
  13. Rijnbeek PR, Witsenburg M, Schrama E, Hess J, Kors JA (2001) New normal limits for the pediatric electrocardiogram. Eur Heart J 22:702–711CrossRefGoogle Scholar
  14. Saladin KS (2007) Anatomy and physiology: the unity of form and function, 4th edn. WCB McGraw-Hill, BostonGoogle Scholar
  15. Salim G, Boucher JM (2005) Hidden Markov tree model applied to ECG delineation. IEEE Trans Instrum Meas 54(6):2163–2168CrossRefGoogle Scholar
  16. Shen TW, Tompkins WJ, Hu YH (2002) One-lead ECG for identity verification. In: Proceedings second joint EMBS/BMES conference, pp 62–63Google Scholar
  17. Sun Y, Chan KL, Krishnan SM (2005) Characteristic wave detection in ECG signal using morphological transform. BMC Cardiovasc Disord 28:1417–2261Google Scholar
  18. 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):16–21CrossRefGoogle Scholar
  19. The CSE Working Party (1985) Recommendations for measurement standards in quantitative electrocardiography. Eur Heart J 6:815–825Google Scholar
  20. 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
  21. Yanushkevich SN (2006) Synthetic biometrics: a survey. In: Proceedings of international joint conference on neural networks (IJCNN), pp 676–683Google Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Institute of Engineering and TechnologyUP Technical University LucknowLucknowIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of Technology KanpurKanpurIndia

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