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Cardiac Murmur Effects on Automatic Segmentation of ECG Signals for Biometric Identification: Preliminary Study

  • C. Duque-Mejía
  • M. A. BecerraEmail author
  • C. Zapata-Hernández
  • C. Mejia-Arboleda
  • A. E. Castro-Ospina
  • E. Delgado-Trejos
  • Diego H. Peluffo-Ordóñez
  • P. Rosero-Montalvo
  • Javier Revelo-Fuelagán
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11431)

Abstract

Biometric identification or authentication is a pattern recognition process, which is carried out acquiring different measures of human beings to distinguish them. Fingerprint and eye iris are the most known and used biometric techniques; nevertheless, also they are the most vulnerable to counterfeiting. Consequently, nowadays research has been focused on physiological signals and behavioral traits for biometric identification because these allow not only the authentication but also determine that the subject is alive. Electrocardiographic signals (ECG-S) have been studied for biometric identification demonstrating their capability. Taking into account that some pathologies are detected using ECG-S, these can affect the results of biometric identification; nonetheless, some diseases such as cardiac murmurs are not detected by ECG-S, but they can distort their morphology. Therefore, these signals must be analyzed considering different pathologies. In this paper, a biometric study was carried out from 40 subjects (20 with cardiac murmurs and 20 without cardiac affections). First, the ECG-S were preprocessed and segmented using the fast method for detecting T waves with annotation of P and T waves, then feature extraction was carried out using discrete wavelet transform (DWT), maximal overlap DWT, cepstral coefficients, and statistical measures. Then, rough set and relief F algorithms were applied to datasets (pathological and normal signals) for attribute reduction. Finally, multiple classifiers and combinations of them were tested. The results of the segmentation were analyzed achieving low results for signals affected by cardiac murmurs. On the other hand, according to the cardiac murmur effects analyzed, the performance of the classifiers in cascade shown the best accuracy for human identification from ECG-S, minimizing the impact of variability generated on ECG-S by cardiac murmurs diseases.

Keywords

Automatic segmentation Biometric Heart murmur Electrocardiographic signal Pattern recognition 

Notes

Acknowledgment

The authors acknowledge to the research project titled “Desarrollo de una metodología de visualización interactiva y eficaz de información en Big Data” supported by Agreement No. 180 November 1st, 2016 by VIPRI from Universidad de Nariño. Besides, acknowledge to SDAS Research Group and to the project P17202 supported by Instituto Tecnologico Metropolitano ITM of Medellin.

References

  1. 1.
    Assadi, I., Charef, A., Mentouri, F., El-bey, R.A.: QRS Complex Based Human Identification, pp. 248–252 (2015)Google Scholar
  2. 2.
    Becerra, M.A., et al.: Exploratory study of the effects of cardiac murmurs on electrocardiographic-signal-based biometric systems. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A.J. (eds.) IDEAL 2018. LNCS, vol. 11314, pp. 410–418. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-03493-1_43CrossRefGoogle Scholar
  3. 3.
    Becerra, M.A., Orrego, D.A., Delgado-Trejos, E.: Adaptive neuro-fuzzy inference system for acoustic analysis of 4-channel phonocardiograms using empirical mode decomposition. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 969–972. IEEE, July 2013.  https://doi.org/10.1109/EMBC.2013.6609664
  4. 4.
    Belgacem, N., Fournier, R., Nait-Ali, A., Bereksi-Reguig, F.: A novel biometric authentication approach using ECG and EMG signals. J. Med. Eng. Technol. 39(4), 226–238 (2015).  https://doi.org/10.3109/03091902.2015.1021429CrossRefGoogle Scholar
  5. 5.
    Bhatnagar, S.: Cooperative biometric multimodal approach for identification. In: Satapathy, S.C., Joshi, A. (eds.) ICTIS 2017. SIST, vol. 83, pp. 13–19. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-63673-3_2CrossRefGoogle Scholar
  6. 6.
    Bugdol, M.D., Mitas, A.W.: Multimodal biometric system combining ECG and sound signals. Pattern Recogn. Lett. 38, 107–112 (2014).  https://doi.org/10.1016/J.PATREC.2013.11.014CrossRefGoogle Scholar
  7. 7.
    Chun, S.Y.: Single Pulse ECG-based Small Scale User Authentication using Guided FilteringGoogle Scholar
  8. 8.
    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: 2015 5th International Conference on IT Convergence and Security, ICITCS 2015 - Proceedings (2015).  https://doi.org/10.1109/ICITCS.2015.7292977
  9. 9.
    Duffy, V.G. (ed.): DHM 2017. LNCS, vol. 10287. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-58466-9CrossRefGoogle Scholar
  10. 10.
    Elgendi, M., Eskofier, B., Abbott, D.: Fast T wave detection calibrated by clinical knowledge with annotation of P and T waves. Sensors 15(7), 17693–17714 (2015).  https://doi.org/10.3390/s150717693CrossRefGoogle Scholar
  11. 11.
    Elhoseny, M., Essa, E., Elkhateb, A., Hassanien, A.E., Hamad, A.: Cascade multimodal biometric system using fingerprint and iris patterns. In: Hassanien, A.E., Shaalan, K., Gaber, T., Tolba, M.F. (eds.) AISI 2017. AISC, vol. 639, pp. 590–599. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-64861-3_55CrossRefGoogle Scholar
  12. 12.
    Ferdinando, H., Seppänen, T., Alasaarela, E.: Bivariate Empirical Mode Decomposition for ECG - based Biometric Identification with Emotional Data, pp. 450–453 (2017)Google Scholar
  13. 13.
    Hejazi, M., Al-Haddad, S.A., Hashim, S.J., Aziz, A.F.A., Singh, Y.P.: Feature level fusion for biometric verification with two-lead ECG signals. In: Proceeding - 2016 IEEE 12th International Colloquium on Signal Processing and its Applications, CSPA 2016, pp. 54–59, March 2016.  https://doi.org/10.1109/CSPA.2016.7515803
  14. 14.
    Hejazi, M., Al-Haddad, S.A., Singh, Y.P., Hashim, S.J., Abdul Aziz, A.F.: ECG biometric authentication based on non-fiducial approach using kernel methods. Digital Signal Process.: Rev. J. 52, 72–86 (2016).  https://doi.org/10.1016/j.dsp.2016.02.008CrossRefGoogle Scholar
  15. 15.
    da Silva, H.P., Carreiras, C., Lourenco, A., Fred, A., das Neves, R.C., Ferreira, R.: Off-the-person electrocardiography: performance assessment and clinical correlation. Health Technol. 4, 309–318 (2015)CrossRefGoogle Scholar
  16. 16.
    Jung, W.H., Lee, S.G.: ECG identification based on non-fiducial feature extraction using window removal method. Appl. Sci. 7(12), 1205 (2017).  https://doi.org/10.3390/app7111205. http://www.mdpi.com/2076-3417/7/11/1205CrossRefGoogle Scholar
  17. 17.
    Kanchan, T., Krishan, K.: Loss of fingerprints: forensic implications. Egypt. J. Forensic Sci. 8(1), 19 (2018).  https://doi.org/10.1186/s41935-018-0051-0CrossRefGoogle Scholar
  18. 18.
    Martinez-Diaz, M., Fierrez, J., Galbally, J., Ortega-Garcia, J.: An evaluation of indirect attacks and countermeasures in fingerprint verification systems. Pattern Recognit. Lett. 32(12), 1643–1651 (2011).  https://doi.org/10.1016/J.PATREC.2011.04.005CrossRefGoogle Scholar
  19. 19.
    Moreno-Revelo, M., Ortega-Adarme, M., Peluffo-Ordoñez, D.H., Alvarez-Uribe, K.C., Becerra, M.A.: Comparison among physiological signals for biometric identification. In: Yin, H., Gao, Y., Chen, S., Wen, Y., Cai, G., Gu, T., Du, J., Tallón-Ballesteros, A.J., Zhang, M. (eds.) IDEAL 2017. LNCS, vol. 10585, pp. 436–443. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-68935-7_47CrossRefGoogle Scholar
  20. 20.
    Murillo-Escobar, M., Cruz-Hernández, C., Abundiz-Pérez, F., López-Gutiérrez, R.: A robust embedded biometric authentication system based on fingerprint and chaotic encryption. Expert Syst. Appl. 42(21), 8198–8211 (2015).  https://doi.org/10.1016/j.eswa.2015.06.035CrossRefGoogle Scholar
  21. 21.
    Nawal, M., Sharma, M.K., Bundele, M.M.: Design and implementation of human identification through physical activity aware 12 lead ECG. In: 2016 International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2016 (2017).  https://doi.org/10.1109/ICRAIE.2016.7939536
  22. 22.
    Odinaka, I., Lai, P.H., Kaplan, A.D., O’Sullivan, J.A., Sirevaag, E.J., Rohrbaugh, J.W.: ECG biometric recognition: a comparative analysis (2012).  https://doi.org/10.1109/TIFS.2012.2215324CrossRefGoogle Scholar
  23. 23.
    Orrego, D., Becerra, M., Delgado-Trejos, E.: Dimensionality reduction based on fuzzy rough sets oriented to ischemia detection. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2012).  https://doi.org/10.1109/EMBC.2012.6347186
  24. 24.
    Pal, S., Mitra, M.: Increasing the accuracy of ECG based biometric analysis by data modelling. Measurement 45(7), 1927–1932 (2012).  https://doi.org/10.1016/J.MEASUREMENT.2012.03.005CrossRefGoogle Scholar
  25. 25.
    Patro, K., Kumar, P.: Machine learning classification approaches for biometric recognition system using ECG signals. Eng. Sci. Technol. Rev. 10(6), 1–8 (2017).  https://doi.org/10.25103/jestr.106.01CrossRefGoogle Scholar
  26. 26.
    Pinto, J., Cardoso, J., Lourenço, A., Carreiras, C.: Towards a continuous biometric system based on ECG signals acquired on the steering wheel. Sensors 17(10), 2228 (2017).  https://doi.org/10.3390/s17102228. http://www.mdpi.com/1424-8220/17/10/2228CrossRefGoogle Scholar
  27. 27.
    Ramli, D.A., Hooi, M.Y., Chee, K.J.: Development of heartbeat detection kit for biometric authentication system. Procedia Comput. Sci. 96, 305–314 (2016).  https://doi.org/10.1016/j.procs.2016.08.143CrossRefGoogle Scholar
  28. 28.
    Sidek, K.A., Khalil, I., Jelinek, H.F.: ECG biometric with abnormal cardiac conditions in remote monitoring system. IEEE Trans. Syst. Man Cybern.: Syst. 44(11), 1498–1509 (2014).  https://doi.org/10.1109/TSMC.2014.2336842CrossRefGoogle Scholar
  29. 29.
    Song, W., Kim, T., Kim, H.C., Choi, J.H., Kong, H.J., Lee, S.R.: A finger-vein verification system using mean curvature. Pattern Recognit. Lett. 32(11), 1541–1547 (2011).  https://doi.org/10.1016/J.PATREC.2011.04.021CrossRefGoogle Scholar
  30. 30.
    Tan, R., Perkowski, M.: Toward improving electrocardiogram (ECG) biometric verification using mobile sensors: a two-stage classifier approach. Sensors (Switzerland) 17(2) (2017).  https://doi.org/10.3390/s17020410CrossRefGoogle Scholar
  31. 31.
    Tseng, K.K., Lee, D., Hurst, W., Lin, F.Y., Ip, W.H.: Frequency rank order statistic with unknown neural network for ECG identification system. In: Proceedings - 4th International Conference on Enterprise Systems: Advances in Enterprise Systems, ES 2016, pp. 160–167 (2017).  https://doi.org/10.1109/ES.2016.27
  32. 32.
    Wahabi, S., Pouryayevali, S., Hari, S., Hatzinakos, D.: On evaluating ECG biometric systems: session-dependence and body posture. IEEE Trans. Inf. Forensics Secur. 9(11), 2002–2013 (2014).  https://doi.org/10.1109/TIFS.2014.2360430CrossRefGoogle Scholar
  33. 33.
    Zhang, Y., Wu, J.: Practical human authentication method based on piecewise corrected Electrocardiogram. In: Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS, vol. 61571268, pp. 300–303 (2017).  https://doi.org/10.1109/ICSESS.2016.7883071
  34. 34.
    Zhao, Z., Yang, L.: ECG identification based on matching pursuit. In: 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), pp. 721–724. IEEE, October 2011.  https://doi.org/10.1109/BMEI.2011.6098470

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • C. Duque-Mejía
    • 1
    • 5
  • M. A. Becerra
    • 1
    • 5
    Email author
  • C. Zapata-Hernández
    • 1
    • 5
  • C. Mejia-Arboleda
    • 2
    • 5
  • A. E. Castro-Ospina
    • 2
    • 5
  • E. Delgado-Trejos
    • 6
  • Diego H. Peluffo-Ordóñez
    • 4
    • 5
  • P. Rosero-Montalvo
    • 3
    • 5
  • Javier Revelo-Fuelagán
    • 4
    • 5
  1. 1.Institución Universitaria Pascual BravoMedellínColombia
  2. 2.Instituto Tecnológico MetropolitanoMedellínColombia
  3. 3.Universidad Técnica del Norte-EcuadorIbarraEcuador
  4. 4.Universidad de NariñoPastoColombia
  5. 5.SDAS Research GroupYachay TechUrcuquíEcuador
  6. 6.Quality, Metrology and Production Research GroupInstituto Tecnológico Metropolitano ITMMedellínColombia

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