Skip to main content
SpringerLink
Log in

Compressive Sensing Based ECG Biometric System

  • Conference paper
  • First Online:
Intelligent Systems and Applications (IntelliSys 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 869))

Included in the following conference series:

Abstract

The Internet of Things (IoT) has started redesigning the paradigm of the connected health sector by leveraging the availability of low power, low-cost sensors and efficient communication protocols. Consequently, IoT based connected health platforms are expected to further enhance the patient connectivity and everyday convenience. Nevertheless, issues related to power consumption and user security limit the performance of such systems. The conventional approaches that incorporate biometric measures into the IoT design rise high concerns regarding the cost and the complexity of the implementation. This paper proposes an identification approach integrated within a patient’s heart monitoring system based on the theory of compressive sensing (CS). CS is an emerging theory that promotes both power optimization and security by transmitting random measurements with fewer samples rather than transmitting the whole raw signal. The proposed system uses the electrocardiogram (ECG) as a biometric measure to identify the patient. The advantage of such system is that it does not require any additional complexity to acquire and process the data. The obtained results showed a successful identification rate up to 98.88% by compressing the transmitted signal to only half the original one.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Islam, S.R., Kwak, D., Kabir, M.H., Hossain, M., Kwak, K.-S.: The internet of things for health care: a comprehensive survey. IEEE Access 3, 678–708 (2015)

    Article  Google Scholar 

  2. Candès, E.J., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theor. 52(2), 489–509 (2006)

    Article  MathSciNet  Google Scholar 

  3. Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theor. 52(4), 1289–1306 (2006)

    Article  Google Scholar 

  4. Candes, E.J., Tao, T.: Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans. Inf. Theor. 52(12), 5406–5425 (2006)

    Article  MathSciNet  Google Scholar 

  5. Biel, L., Pettersson, O., Philipson, L., Wide, P.: ECG analysis: a new approach in human identification. IEEE Trans. Instrum. Measur. 50(3), 808–812 (2001)

    Article  Google Scholar 

  6. Irvine, J., Wiederhold, B., Gavshon, L., Israel, S., McGehee, S., Meyer, R., Wiederhold, M.: Heart rate variability: a new biometric for human identification. In: Proceedings of the International Conference on Artificial Intelligence (IC-AI01), pp. 1106–1111 (2001)

    Google Scholar 

  7. Kyoso, M., Uchiyama, A.: Development of an ECG identification system. In: Engineering in Medicine and Biology Society: Proceedings of the 23rd Annual International Conference, vol. 4, pp. 3721–3723. IEEE (2001)

    Google Scholar 

  8. 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)

    Article  Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. Plataniotis, K.N., Hatzinakos, D., Lee, J.K.: ECG biometric recognition without fiducial detection. In: Biometric Consortium Conference: Biometrics Symposium, pp. 1–6. IEEE (2006)

    Google Scholar 

  11. Safie, S.I., Soraghan, J.J., Petropoulakis, L.: Electrocardiogram (ECG) biometric authentication using pulse active ratio (par). IEEE Trans. Inf. Forensics Secur. 6(4), 1315–1322 (2011)

    Article  Google Scholar 

  12. Gahi, Y., Lamrani, M., Zoglat, A., Guennoun, M., Kapralos, B., El-Khatib, K.: Biometric identification system based on electrocardiogram data. In: New Technologies, Mobility and Security, NTMS’08, pp. 1–5. IEEE (2008)

    Google Scholar 

  13. Venkatesh, N., Jayaraman, S.: Human electrocardiogram for biometrics using dtw and flda. In: 20th International Conference on Pattern Recognition (icpr), pp. 3838–3841. IEEE (2010)

    Google Scholar 

  14. Odinaka, I., Lai, P.-H., Kaplan, A.D., O’Sullivan, J.A., Sirevaag, E.J., Kristjansson, S.D., Sheffield, A.K., Rohrbaugh, J.W.: ECG biometrics: a robust short-time frequency analysis. In: IEEE International Workshop on Information Forensics and Security (wifs), pp. 1–6. IEEE (2010)

    Google Scholar 

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

    Article  Google Scholar 

  16. Boumbarov, O., Velchev, Y., Sokolov, S.: ECG personal identification in subspaces using radial basis neural networks. In: IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, pp. 446–451. IEEE (2009)

    Google Scholar 

  17. Wao, J., Wan, Y.: Improving computing efficiency of a wavelet method using ECG as a biometric modality. Int. J. Comput. Netw. Secur. 2(1), 15–20 (2010)

    Google Scholar 

  18. Agrafioti, F., Hatzinakos, D.: ECG biometric analysis in cardiac irregularity conditions. Signal Image Video Process. 3(4), 329 (2009)

    Article  Google Scholar 

  19. Ghofrani, N., Bostani, R.: Reliable features for an ECG-based biometric system. In: 17th Iranian Conference of Biomedical Engineering (ICBME), pp. 1–5. IEEE (2010)

    Google Scholar 

  20. Agrafioti, F., Bui, F.M., Hatzinakos, D.: Medical biometrics: the perils of ignoring time dependency. In: IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6. IEEE (2009)

    Google Scholar 

  21. Odinaka, I., Lai, P.-H., Kaplan, A.D., O’Sullivan, J.A., Sirevaag, E.J., Rohrbaugh, J.W.: ECG biometric recognition: a comparative analysis. IEEE Trans. Inf. Forensics Secur. 7(6), 1812–1824 (2012)

    Article  Google Scholar 

  22. Hejazi, M., Al-Haddad, S.A.R., Singh, Y.P., Hashim, S.J., Aziz, A.F.A.: ECG biometric authentication based on non-fiducial approach using kernel methods. Digital Signal Process. 52, 72–86 (2016)

    Article  Google Scholar 

  23. Coutinho, D.P., Silva, H., Gamboa, H., Fred, A., Figueiredo, M.: Novel fiducial and non-fiducial approaches to electrocardiogram-based biometric systems. IET Biometrics 2(2), 64–75 (2013)

    Article  Google Scholar 

  24. 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: 5th International Conference on IT Convergence and Security (ICITCS), pp. 1–5. IEEE (2015)

    Google Scholar 

  25. Dar, M.N., Akram, M.U., Usman, A., Khan, S.A.: ECG biometric identification for general population using multiresolution analysis of dwt based features. In: Second International Conference on Information Security and Cyber Forensics (InfoSec), pp. 5–10. IEEE (2015)

    Google Scholar 

  26. Candès, E.J., Wakin, M.B.: An introduction to compressive sampling. IEEE Signal Process. Mag. 25(2), 21–30 (2008)

    Article  Google Scholar 

  27. Muthukrishnan, S.: Data streams: Algorithms and applications. Now Publishers Inc (2005)

    Google Scholar 

  28. Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM J. Sci. Comput. 20(1), 33–61 (1998)

    Article  MathSciNet  Google Scholar 

  29. Candes, E.J.: The restricted isometry property and its implications for compressed sensing. C. R. Math. 346(9), 589–592 (2008)

    Article  MathSciNet  Google Scholar 

  30. Candes, E., Tao, T.: The dantzig selector: statistical estimation when p is much larger than n. The Annals of Statistics, pp. 2313–2351 (2007)

    Google Scholar 

  31. Mallat, S.G., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 41(12), 3397–3415 (1993)

    Article  Google Scholar 

  32. Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theor. 53(12), 4655–4666 (2007)

    Article  MathSciNet  Google Scholar 

  33. Needell, D., Tropp, J.A.: Cosamp: iterative signal recovery from incomplete and inaccurate samples. Appl. Comput. Harmonic Anal. 26(3), 301–321 (2009)

    Article  MathSciNet  Google Scholar 

  34. Dai, W., Milenkovic, O.: Subspace pursuit for compressive sensing signal reconstruction. IEEE Trans. Inf. Theor. 55(5), 2230–2249 (2009)

    Article  MathSciNet  Google Scholar 

  35. Donoho, D.L., Tsaig, Y., Drori, I., Starck, J.-L.: Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit. IEEE Trans. Inf. Theor. 58(2), 1094–1121 (2012)

    Article  MathSciNet  Google Scholar 

  36. Blanchard, J.D., Tanner, J.: Performance comparisons of greedy algorithms in compressed sensing. Numer. Linear Algebra Appl. 22(2), 254–282 (2015)

    Article  MathSciNet  Google Scholar 

  37. Foucart, S., Rauhut, H.: A mathematical introduction to compressive sensing, vol. 1, no. 3. Birkhäuser Basel (2013)

    Google Scholar 

  38. Jacques, L., Laska, J.N., Boufounos, P.T., Baraniuk, R.G.: Robust 1-bit compressive sensing via binary stable embeddings of sparse vectors. IEEE Trans. Inf. Theor. 59(4), 2082–2102 (2013)

    Article  MathSciNet  Google Scholar 

  39. Liaw, Y.-C., Wu, C.-M., Leou, M.-L.: Fast k-nearest neighbors search using modified principal axis search tree. Digital Signal Process. 20(5), 1494–1501 (2010)

    Article  Google Scholar 

  40. Fatemian, S.Z., Hatzinakos, D.: A new ECG feature extractor for biometric recognition. In: 16th International Conference on Digital Signal Processing, pp. 1–6. IEEE (2009)

    Google Scholar 

  41. Pan, J., Tompkins, W.J.: A real-time qrs detection algorithm. IEEE Trans. Biomed. Eng. 3, 230–236 (1985)

    Article  Google Scholar 

  42. Lugovaya, T.S.: Biometric Human Identification Based on ECG (2005)

    Google Scholar 

  43. Salloum, R., Kuo, C.-C.J.: ECG-based biometrics using recurrent neural networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2062–2066. IEEE (2017)

    Google Scholar 

  44. Patro, K.K., Kumar, P.R.: Effective feature extraction of ecg for biometric application. Procedia Comput. Sci. 115, 296–306 (2017)

    Article  Google Scholar 

Download references

Acknowledgment

This paper was made possible by National Priorities Research Program (NPRP) grant No. 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

Author information

Authors and Affiliations

  1. College of Engineering, Qatar University, P.O. Box: 2713, Doha, Qatar

    Hamza Djelouat, Mohammed Al Disi, Abbes Amira & Faycal Bensaali

  2. College of Engineering and Technology, University of Derby, Derby, DE22 1GB, UK

    Xiaojun Zhai

Authors
  1. Hamza Djelouat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammed Al Disi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abbes Amira
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Faycal Bensaali
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaojun Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamza Djelouat .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Djelouat, H., Al Disi, M., Amira, A., Bensaali, F., Zhai, X. (2019). Compressive Sensing Based ECG Biometric System. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_11

Download citation

Publish with us

Policies and ethics

Search

Navigation