Skip to main content

Advertisement

SpringerLink
Log in

Compressed ECG Biometric: A Fast, Secured and Efficient Method for Identification of CVD Patient

  • Original Paper
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Adoption of compression technology is often required for wireless cardiovascular monitoring, due to the enormous size of Electrocardiography (ECG) signal and limited bandwidth of Internet. However, compressed ECG must be decompressed before performing human identification using present research on ECG based biometric techniques. This additional step of decompression creates a significant processing delay for identification task. This becomes an obvious burden on a system, if this needs to be done for a trillion of compressed ECG per hour by the hospital. Even though the hospital might be able to come up with an expensive infrastructure to tame the exuberant processing, for small intermediate nodes in a multihop network identification preceded by decompression is confronting. In this paper, we report a technique by which a person can be identified directly from his / her compressed ECG. This technique completely obviates the step of decompression and therefore upholds biometric identification less intimidating for the smaller nodes in a multihop network. The biometric template created by this new technique is lower in size compared to the existing ECG based biometrics as well as other forms of biometrics like face, finger, retina etc. (up to 8302 times lower than face template and 9 times lower than existing ECG based biometric template). Lower size of the template substantially reduces the one-to-many matching time for biometric recognition, resulting in a faster biometric authentication mechanism.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Biel, L., Petersson, O., Philipson, L., and Wide, P., Ecg analysis: A new approach in human identification. IEEE Trans. Instrum. Meas. 50(3):808–812, 2001.

    Article  Google Scholar 

  2. Blount M. et. al, Remote health-care monitoring using personal care connect. IBM Syst. J. 46(1):95–113, 2007.

    Article  Google Scholar 

  3. Bui, F. M., and Hatzinakos, D., Biometric methods for secure communications in body sensor networks: Resource-efficient key management and signal-level data scrambling. EURASIP J. Adv. Signal Process. 2008:Article ID 529879, 2008.

  4. Chan, A. D. C., Hamdy, M. M., Badre, A., and Badee, V., Wavelet distance measure for person identification using electrocardiograms. IEEE Trans. Instrum. Meas. 57(2):248–253, 2008.

    Article  Google Scholar 

  5. Hamilton, P. S., and Tompkins, W. J., Quantitative investigation of qrs detection rules using the mit/bih arrhythmia database. IEEE Trans. Biomed. Eng. BME-33(12):1157–1165, 1986.

    Article  Google Scholar 

  6. Hung, K., and Zhang, Y.-T., Implementation of a wap-based telemedicine system for patient monitoring. IEEE Trans. Inf. Technol. Biomed. 7(2):101–107, 2003.

    Article  Google Scholar 

  7. Irvine, J. M., et al, Heart rate variability: a new biometric for human identification. In: Int. Conf. on Arti. Intell., Las Vegas, Nevada, 2001. pp. 1106–1111, 2001.

  8. Israel, S. A., Irvine, J. A., Cheng, A., and Wiederhold, B. K., Ecg to identify individuals. Pattern Recogn. 38(1):133–142, 2005.

    Article  Google Scholar 

  9. Khalil, I., and Sufi, F., Legendre polynomials based biometric authentication using qrs complex of ECG. In: International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 2008. ISSNIP 2008. pp. 297–302, 2008.

  10. Lee, R.-G., Chen, K.-C., Hsiao, C.-C., and Tseng, C.-L., A mobile care system with alert mechanism. IEEE Trans. Inf. Technol. Biomed. 11(5):507–517, 2007.

    Article  Google Scholar 

  11. Mahmood, A. N., Leckie, C. and Udaya, P. Echidna: Efficient Clustering of Hierarchical Data for Network Traffic Analysis. In: Proceedings of the fifth IFIP Networking Conference (Networking 2006): LNCS, Springer Verlag, pp. 1092–1098, 2006.

  12. Mahmood, A. N., Leckie, C. and Udaya, P. An efficient clustering scheme to exploit hierarchical data in network traffic analysis., IEEE Transactions on Knowledge and Data Engineering (TKDE) 20(6):752–767, 2008.

    Article  Google Scholar 

  13. Online, Health insurance portability accountability act of 1996 (hipaa), centers for medicare and medicaid services (1996). Available at: http://www.cms.hhs.gov/hipaageninfo, accessed 2008.

  14. Online, Cardionet: Get to the heart of the problem. Available at http://www.cardionet.com/, accessed 2009.

  15. Online, Physiobank: Physiologic signal archives for biomedical research. Available at http://www.physionet.org/physiobank/, accessed 2009.

  16. Poon, C. C. Y., Zhang, Y. T., and Bao, S. D., A novel biometric method to secure wireless body area sensor networks for telemedicine and m-health. IEEE Commun. Mag. 44:73–81, 2006.

    Article  Google Scholar 

  17. Sufi, F., Mobile phone programming java 2 micro edition. In: Proceedings of the 2007 International Workshop on Mobile Computing Technologies for Pervasive Healthcare, Philip Island, Melbourne. pp. 64–80, 2007.

  18. Sufi, F., Fang, Q., Khalil, I., and Mahmoud, S. S., Novel methods of faster cardiovascular diagnosis in wireless telecardiology. IEEE J. Sel. Areas Commun. 27(4), 2009.

  19. Sufi, F., Fang, Q., Mahmoud, S., and Cosic, I., A mobile phone based intelligent telemonitoring platform. In: Medical Devices and Biosensors, 2006. 3rd IEEE/EMBS International Summer School on ISSMDBS. pp. 101–104, 2006.

  20. Sufi, F., and Khalil, I., An automated patient authentication system for remote telecardiology. In: International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 2008. ISSNIP 2008. pp. 279–284, 2008.

  21. Sufi, F., and Khalil, I., Enforcing secured ECG transmission for realtime telemonitoring: A joint encoding, compression, encryption mechanism. Security and communication networks. Security and Communication Networks 1(5):389–405, 2008.

    Article  Google Scholar 

  22. Sufi, F., and Khalil, I., A new feature detection mechanism and its application in secured ECG transmission with noise masking. J. Med. Syst. 33(3):121–132, 2009.

    Article  Google Scholar 

  23. Sufi, F., Khalil, I., and Habib, I., Polynomial distance measurement for ECG based biometric authentication (accepted and in press). Security and Communication Networks. 2009. doi:10.1002/sec.76.

  24. Sufi, F., Khalil, I., and Hu, J., In: Stavroulakis, P. (Eds.), Ecg based biometric: The Next Generation in Human Identification. New York: Springer, 2009.

    Google Scholar 

  25. Sufi, F., Mahmoud, S., and Khalil, I., A novel wavelet packet based anti spoofing technique to secure ECG data. Int. J. of Biometrics 1(2):191–208, 2008.

    Article  Google Scholar 

  26. Wubbeler, G., Stavridis, M., Kreiseler, D., Bousseljot, R. D., and Elster, C., Verification of humans using the electrocardiogram. Pattern Recogn. Lett. 28:1172–1175, 2007.

    Article  Google Scholar 

  27. Yu, F., Tang, H., Leung, V., Liu, J., and Lung, C., Biometric-based user authentication in mobile ad hoc networks. Security and Communication Networks 1:5–16, 2008.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Information Technology, RMIT University, Melbourne, Australia

    Fahim Sufi, Ibrahim Khalil & Abdun Mahmood

Authors
  1. Fahim Sufi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ibrahim Khalil
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdun Mahmood
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fahim Sufi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sufi, F., Khalil, I. & Mahmood, A. Compressed ECG Biometric: A Fast, Secured and Efficient Method for Identification of CVD Patient. J Med Syst 35, 1349–1358 (2011). https://doi.org/10.1007/s10916-009-9412-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-009-9412-4

Keywords

Search

Navigation