Advertisement

Journal of Medical Systems

, 39:148 | Cite as

Human Identification Using Compressed ECG Signals

  • Carmen Camara
  • Pedro Peris-LopezEmail author
  • Juan E. Tapiador
Patient Facing Systems
Part of the following topical collections:
  1. Smart Living in Healthcare and Innovations

Abstract

As a result of the increased demand for improved life styles and the increment of senior citizens over the age of 65, new home care services are demanded. Simultaneously, the medical sector is increasingly becoming the new target of cybercriminals due the potential value of users’ medical information. The use of biometrics seems an effective tool as a deterrent for many of such attacks. In this paper, we propose the use of electrocardiograms (ECGs) for the identification of individuals. For instance, for a telecare service, a user could be authenticated using the information extracted from her ECG signal. The majority of ECG-based biometrics systems extract information (fiducial features) from the characteristics points of an ECG wave. In this article, we propose the use of non-fiducial features via the Hadamard Transform (HT). We show how the use of highly compressed signals (only 24 coefficients of HT) is enough to unequivocally identify individuals with a high performance (classification accuracy of 0.97 and with identification system errors in the order of 10−2).

Keywords

Healthcare Biometrics Human Identification and ECG 

Notes

Acknowledgements

This work was supported by the MINECO grant TIN2013-46469-R (SPINY: Security and Privacy in the Internet of You) and the CAM grant S2013/ICE-3095 (CIBERDINE: Cybersecurity, Data, and Risks).

Conflict of interests

The author declares that they have no conflict of interest.

References

  1. 1.
    Agrafioti, F., and Hatzinakos, D.: ECG based recognition using second order statistics. In: 6th Annual Conference on Communication Networks and Services Research (CNSR), pp. 82–87 (2008)Google Scholar
  2. 2.
    Cempirek, M., and Stastny, J., The optimization of the EEG-based biometric classification. Applied Electronics, 25–28, 2007.Google Scholar
  3. 3.
    Identity Theft Resource Center. Data breach report. Technical report (December 2014)Google Scholar
  4. 4.
    Chan, A. D. C., Hamdy, M. M., Badre, A., Badee, V., Wavelet distance measure for person identification using electrocardiograms. IEEE Trans. Instrum. Meas. 57(2):248–253, Feb 2008.CrossRefGoogle Scholar
  5. 5.
    Luz, E. J. da S., Menotti, D., Robson Schwartz, W., Evaluating the use of {ECG} signal in low frequencies as a biometry. Expert Systems with Applications 41(5):2309–2315, 2014.CrossRefGoogle Scholar
  6. 6.
    Frank, M., Biedert, R., Ma, E., Martinovic, I., Touchalytics, D. Song., On the applicability of touchscreen input as a behavioral biometric for continuous authentication. IEEE Transactions on Information Forensics and Security 8(1):136–148, 2013.CrossRefGoogle Scholar
  7. 7.
    Gahi, Y., Lamrani, M., Zoglat, A., Guennoun, M., Kapralos, B., El-Khatib, K.: Biometric identification system based on electrocardiogram data. In: Int. Conference on new technologies, mobility and security (NTMS), pp. 1–5 (2008)Google Scholar
  8. 8.
    Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. Ch., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., Stanley, H. E., PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220, June 13. doi: 10.1161/01.CIR.101.23.e215.. Circulation Electronic Pages: http://circ.ahajournals.org/cgi/content/full/101/23/e215PMID:1085218.
  9. 9.
    Inthavisas, K., and Lopresti, D., Secure speech biometric templates for user authentication. IET Biometrics 1(1):46–54, 2012.CrossRefGoogle Scholar
  10. 10.
    Israel, S. A., Irvine, J. M., Cheng, A., Wiederhold, M. D., Wiederhold, B. K., Ecg to identify individuals. Pattern Recogn. 38(1):133–142, 2005.CrossRefGoogle Scholar
  11. 11.
    Jain, A. K., Ross, A., Pankanti, S., Biometrics: a tool for information security. IEEE Transactions on Information Forensics and Security 1(2):125–143, 2006.CrossRefGoogle Scholar
  12. 12.
    Khalifa, W., Salem, A., Roushdy, M.: A survey of eeg based user authentication schemes. In: 8th International Conference on Informatics and Systems, pp. 55–60 (May 2012)Google Scholar
  13. 13.
    Kiranyaz, S., Ince, T., Pulkkinen, J., Gabbouj, M., Personalized long-term ecg classification: A systematic approach. Expert Systems with Applications 38(4):3220–3226, 2011.CrossRefGoogle Scholar
  14. 14.
    Kumari, P., and Vaish, A., Brainwave based user identification system: A pilot study in robotics environment. Robot. Auton. Syst. 65(0):15–23, 2015.CrossRefGoogle Scholar
  15. 15.
    Mehrotra, H., Rattani, A., Gupta, P.: Fusion of iris and fingerprint biometric for recognition. In: Proceedings of the International Conference on Signal and Image Processing, pp. 1–6 (2006)Google Scholar
  16. 16.
    Miller, B., Vital signs of identity [biometrics]. IEEE Spectrum 31(2):22–30, Feb 1994.CrossRefGoogle Scholar
  17. 17.
    Nait-Ali, A.: Beyond classical biometrics: When using hidden biometrics to identify individuals, pp. 241–246 (2011)Google Scholar
  18. 18.
    Odinaka, I., Po-Hsiang, L., Kaplan, A.D., O’Sullivan, J. A., Sirevaag, E. J., recognition, J. W. Rohrbaugh. Ecg biometric, A comparative analysis. IEEE Transactions on Information Forensics and Security 7(6): 1812–1824, Dec 2012.CrossRefGoogle Scholar
  19. 19.
    Pal, S., and Mitra, M., Increasing the accuracy of {ECG} based biometric analysis by data modelling. Measurement 45(7):1927–1932, 2012.CrossRefGoogle Scholar
  20. 20.
    Palaniappan, R., Multiple mental thought parametric classification: A new approach for individual identification. Journal of Information and Communication Engineering 2(4), 2006.Google Scholar
  21. 21.
    Rasmussen, K. B., Roeschlin, M., Martinovic, I., Tsudik, G.: Authentication using pulse-response biometrics. In The Network and Distributed System Security Symposium (NDSS) (2014)Google Scholar
  22. 22.
    Riera, A., Dunne, S., Cester, I., Ruffini, G.: STARFAST: a wireless wearable eeg/ecg biometric system based on the ENOBIO sensor. International Workshop on Wearable Micro and Nanosystems for Personalised Health (2008)Google Scholar
  23. 23.
    Saechia, S., Koseeyaporn, J., Wardkein, P., Human identification system based ECG signal. In IEEE TENCON,1–4, 2005.Google Scholar
  24. 24.
    Schneier, B.: Changing passwords. https://www.schneier.com/blog/archives/2010/11/changing_passwo.html(November 2010)
  25. 25.
    Shin, L., How biometrics could improve health security. Fortune, 2015.Google Scholar
  26. 26.
    Silva, H., Gamboa, H., Fred, A., One lead ecg based personal identification with feature subspace ensembles, pp. 770–783. Springer: Berlin Heidelberg, 2007.Google Scholar
  27. 27.
    Sim, H. M., Asmuni, H., Hassan, R., biometrics, R. M. Othman. Multimodal, Weighted score level fusion based on non-ideal iris and face images. Expert Systems with Applications 41(11):5390–5404, 2014.CrossRefGoogle Scholar
  28. 28.
    Singh, Y. N., Singh, S. K., Ray, A. K.: Bioelectrical signals as emerging biometrics Issues and challenges. In: ISRN Signal Processing, Vol. 2012, pp. 1–13 (2012)Google Scholar
  29. 29.
    Sun, S.: Multitask learning for eeg-based biometrics. In: 19th International Conference on Pattern Recognition (ICPR), pp. 1–4 (2008)Google Scholar
  30. 30.
    Suresh, M., Krishnamohan, P. G., Holi, M.S., GMM modeling of person information from EMG signals. IEEE Recent Advances in Intelligent Computational Systems (RAICS),712–717 , 2011.Google Scholar
  31. 31.
    Tantawi, M. M., Revett, K., Tolba, M. F., Salem, A.: On the use of the electrocardiogram for biometrie authentication. In: 8th International Conference on Informatics and Systems, pp. 48–54 (May 2012)Google Scholar
  32. 32.
    Tarricone, R., and Tsouros, A. D: Home Care in Europe: The Solid Facts. WHO Regional Office Europe (2008)Google Scholar
  33. 33.
    Tresadern, P., Cootes, T. F., Poh, N., Matejka, P., Hadid, A., Levy, C., McCool, C., Marcel, S., Mobile biometrics: Combined face and voice verification for a mobile platform. IEEE Pervasive Computing 12(1): 79–87, 2013.CrossRefGoogle Scholar
  34. 34.
    Wang, Y., Agrafioti, F., Hatzinakos, D., Plataniotis, K. N., Analysis of human electrocardiogram for biometric recognition. EURASIP Journal on Advances in Signal Processing, 2008, January 2008.Google Scholar
  35. 35.
    Yang, J., Shi, Y., Yang, J., Personal identification based on finger-vein features. Comput. Hum. Behav. 27 (5):1565–1570, 2011.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Carmen Camara
    • 1
  • Pedro Peris-Lopez
    • 1
    Email author
  • Juan E. Tapiador
    • 1
  1. 1.COSEC Lab (Computer Science Department)Carlos III University of Madrid, Avda de la Universidad 30LeganesSpain

Personalised recommendations