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Express method of biometric person authentication based on one cycle of the ECG signal

НазваExpress method of biometric person authentication based on one cycle of the ECG signal
Назва англійськоюExpress method of biometric person authentication based on one cycle of the ECG signal
АвториSerhii Lupenko, Roman Butsiy
ПринадлежністьFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, Poland Institute of Telecommunications and Global Information Space, National Academy of Sciences of Ukraine, Kyiv, Ukraine
Бібліографічний описExpress method of biometric person authentication based on one cycle of the ECG signal / Serhii Lupenko, Roman Butsiy // Scientific Journal of TNTU. — Tern.: TNTU, 2024. — Vol 113. — No 1. — P. 100–110.
Bibliographic description:Lupenko S., Butsiy R. (2024) Express method of biometric person authentication based on one cycle of the ECG signal. Scientific Journal of TNTU (Tern.), vol 113, no 1, pp. 100–110.
DOI: https://doi.org/10.33108/visnyk_tntu2024.01.100
УДК

519.65

Ключові слова

biometric authentication, electrocardiogram signal, cyclically correlated random process, signals normalization, signals classification.

The article is devoted to an express method of biometric authentication of a person based on an electrocardiogram (ECG). The method is characterized by high accuracy (efficiency) of authentication of a person based on only one cycle of its ECG. Such characteristics as Accuracy, Balanced Accuracy and F1-score on average are not lower than 96.1% for such binary classifiers as k-Nearest Neighbors, Linear SVM, Decision Tree, Random Forest, Multilayer Perceptron, Adaptive Boosting, Naive Bayes and Statistical Interval Classifier. The research utilized the Combined Measurement of ECG, Breathing, and Seismocardiograms database, which features data from 20 healthy people. A method of constructing confidence intervals for ECG cycles has been developed, which is based on the rhythm-adaptive statistical estimation of the mathematical expectation and the standard deviation of the ECG signal. The method of constructing confidence intervals is based on the functioning of the Statistical Interval Classifier in the system of biometric authentication of a person. The Statistical Interval Classifier has the lowest time computational complexity among the 8 studied classifiers, which justifies its use in portable biometric authentication systems that have negligible computing resources.

ISSN:2522-4433
Перелік літератури
1. Park G. Bio-signal and personal authentication. Korea Internet and Security Agency, Technical Report, Aug 2014.
2. Park, G. Analysis of authentication technology using bio-signals and construction of bio-signal database. Korea Internet and Security Agency, Technical Report, Jan. 2016.
3. Kim J., Park G. Personal authentication technology using biosignals and DB construction. TTA J. 2016. 165. Р. 41–46.

4. Paranjape R., Mahovsky J., Benedicenti L., Koles Z. The EEG as a biometric. In Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering, Vancouver, BC, Canada, 13–16 May 2001. Volume 2. P. 1363–1366. Doi: https://doi.org/10.1109/CCECE.2001.933649.

5. Vural E., Simske S., Schuckers S. Verification of individuals from accelerometer measures of cardiac chest movements. In Proceedings of the 2013 International Conference of the BIOSIG, Darmstadt, Germany, 5–6 September 2013. P. 1–8. INSPEC Accession Number: 13826572.
6. Guo H., Cao X., Wu J., Tang J. Ballistocardiogram-based person identification using correlation analysis. In Proceedings of the World Congress on Medical Physics and Biomedical Engineering. Beijing, China. 26–31 May 2012. Volume 39. P. 570–573. Doi: https://doi.org/10.1007/978-3-642-29305-4_149.
7. Plataniotis K., Hatzinaks D., Lee J. ECG biometric recognition without fiducial detection. In Proceedings of the Biometrics Symposium (BSYM '06), Baltimore, MD, USA. 19–21 September 2006. P. 6–11. Doi: https://doi.org/10.1109/BCC.2006.4341628.
8. Wubbeler G., Stavridis M., Kreiseler D., Bousseljot R., Elster C. Verification of humans using the electrocardiogram. Pattern Recognit. Lett. 2007. 28. Р. 1172–1175. Doi: https://doi.org/10.1016/ j.patrec.2007.01.014.
9. Pereira T. M. C., Conceição R. C., Sebastião R. Initial Study Using Electrocardiogram for Authentication and Identification. Sensors 2022, 22, Doi: https://doi.org/10.3390/s22062202.
10. Molina G., Bruekers F., Presura C., Damstra M., Veen M. Morphological synthesis of ECG signals for person authentication. In Proceedings of the 15th European Signal Processing Conference, Poznan, Poland, 3–7 September 2007. P. 738–742.
11. Pathoumvanh S., Airphaiboon S., Hamamoto K. Robustness study of ECG biometric identification in heart rate variability conditions. IEEE Trans. Electric. Electron. Eng. 2014, 9, 294–301. Doi: http://dx.doi.org/ 10.1002/tee.21970.
12. Eberz S., Paoletti N., Roeschlin M., Patani A., Kwiatkowska M., Martinovic I. Broken Hearted: How To Attack ECG Biometrics. In Proceedings of the NDSS, San Diego, CA, USA, 26 February – 1 March 2017. P. 1–15. Doi: http://dx.doi.org/10.14722/ndss.2017.23408.
13. Alves A., Carreiras C. CardioWheel: ECG Biometrics on the Steering Wheel. In Proceedings of the European Conference: Machine Learning and Knowledge Discovery in Databases, Porto, Portugal, 7–11 September 2015. P. 267–270.
14. Fatimah B., Singh P., Singhal A., Pachori R. B. Biometric Identification From ECG Signals Using Fourier Decomposition and Machine Learning. IEEE Trans. Instrum. Meas. 2022, 71, 1–9. Doi: https://doi.org/ 10.1109/TIM.2022.3199260.
15. Homer M., Irvine J. M., Wendelken S. A model-based approach to human identification using ECG. In Proceedings of the SPIE Conference on Optics and Photonics for Global Homeland Security V and Biometric Technology for Human Identification VI, Orlando, FL, USA, 13–17 April 2009. Volume 7306. 730625. Doi: http://dx.doi.org/10.1117/12.819327.
16. Benouis M., Mostefai L., Costen N., Regouid M. ECG-based biometric identification using one-dimensional local difference pattern. Biomed. Signal Process. Control. 2021, 64, 102226. Doi: https: //doi.org/10.1016/j.bspc.2020.102226.
17. Irvine J. M., Wiederhold B. K., Gavshon L. W., Israel S., McGehee S. B., Meyer R., Wiederhold M. D. Heart rate variability: A new biometric for human identification. In Proceedings of the International Conference on Artificial Intelligence (IC-AI'01), Las Vegas, NV, USA, 7–9 November 2001. P. 1106–1111.
18. Wan Y., Yao J. A neural network to identify human subjects with electrocardiogram signals. In Proceedings of the World Congress on Engineering and Computer Science, San Francisco, CA, USA, 22–24 October 2008. P. 1–4.
19. Chan A. D., Hamdy M. M., Badre A., Badee V. Wavelet distance measure for person identification using electrocardiograms. IEEE Trans. Instrum. Meas. 2008. 57. Р. 248–253. Doi: https://doi.org/10.1109/TIM. 2007.909996.
20. Ye C., Coimbra M. T., Kumar B. V. Investigation of human identification using two-lead electrocardiogram (ECG) signals. In Proceedings of the 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), Washington, DC, USA, 27–29 September 2010; P. 1–8. Doi: https://doi.org/10.1109/BTAS.2010.5634478.
21. García-González M. A., Argelagós-Palau A. Data from: combined measurement of ECG, breathing, and Seismocardiograms. PhysioNet. 2013. Doi: https://doi.org/10.13026/C2KW23.
22. García-González M. A., Argelagós-Palau A., Fernández-Chimeno M., Ramos-Castro J. A comparison of heartbeat detectors for the seismocardiogram. In Computing in Cardiology 2013, September 22–25, 2013. P. 461–464.
23. Goldberger A., Amaral L., Glass L., Hausdorff J., Ivanov P. C., Mark R. et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 2000. 101. e215–e220. Doi: https://doi.org/10.1161/01.cir.101.23.e215.
24. Lupenko S. The rhythm-adaptive Fourier series decompositions of cyclic numerical functions and one-dimensional probabilistic characteristics of cyclic random processes. Digital Signal Processing. 2023. 104104, ISSN 1051-2004. Doi: https://doi.org/10.1016/j.dsp.2023.104104.
25. Lupenko S. The Mathematical Model of Cyclic Signals in Dynamic Systems as a Cyclically Correlated Random Process. Mathematics 2022, 10, 3406. Doi: https://doi.org/10.3390/math10183406.
26. Lupenko S., Lytvynenko I., Sverstiuk A., Horkunenko A., Shelestovskyi B. Software for statistical processing and modeling of a set of synchronously registered cardio signals of different physical nature. CEUR Workshop Proceedings. 2021.2864. P. 194–205.
References:
1. Park G. Bio-signal and personal authentication. Korea Internet and Security Agency, Technical Report, Aug 2014.
2. Park, G. Analysis of authentication technology using bio-signals and construction of bio-signal database. Korea Internet and Security Agency, Technical Report, Jan. 2016.
3. Kim J., Park G. Personal authentication technology using biosignals and DB construction. TTA J. 2016. 165. Р. 41–46.

4. Paranjape R., Mahovsky J., Benedicenti L., Koles Z. The EEG as a biometric. In Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering, Vancouver, BC, Canada, 13–16 May 2001. Volume 2. P. 1363–1366. Doi: https://doi.org/10.1109/CCECE.2001.933649.

5. Vural E., Simske S., Schuckers S. Verification of individuals from accelerometer measures of cardiac chest movements. In Proceedings of the 2013 International Conference of the BIOSIG, Darmstadt, Germany, 5–6 September 2013. P. 1–8. INSPEC Accession Number: 13826572.
6. Guo H., Cao X., Wu J., Tang J. Ballistocardiogram-based person identification using correlation analysis. In Proceedings of the World Congress on Medical Physics and Biomedical Engineering. Beijing, China. 26–31 May 2012. Volume 39. P. 570–573. Doi: https://doi.org/10.1007/978-3-642-29305-4_149.
7. Plataniotis K., Hatzinaks D., Lee J. ECG biometric recognition without fiducial detection. In Proceedings of the Biometrics Symposium (BSYM '06), Baltimore, MD, USA. 19–21 September 2006. P. 6–11. Doi: https://doi.org/10.1109/BCC.2006.4341628.
8. Wubbeler G., Stavridis M., Kreiseler D., Bousseljot R., Elster C. Verification of humans using the electrocardiogram. Pattern Recognit. Lett. 2007. 28. Р. 1172–1175. Doi: https://doi.org/10.1016/ j.patrec.2007.01.014.
9. Pereira T. M. C., Conceição R. C., Sebastião R. Initial Study Using Electrocardiogram for Authentication and Identification. Sensors 2022, 22, Doi: https://doi.org/10.3390/s22062202.
10. Molina G., Bruekers F., Presura C., Damstra M., Veen M. Morphological synthesis of ECG signals for person authentication. In Proceedings of the 15th European Signal Processing Conference, Poznan, Poland, 3–7 September 2007. P. 738–742.
11. Pathoumvanh S., Airphaiboon S., Hamamoto K. Robustness study of ECG biometric identification in heart rate variability conditions. IEEE Trans. Electric. Electron. Eng. 2014, 9, 294–301. Doi: http://dx.doi.org/ 10.1002/tee.21970.
12. Eberz S., Paoletti N., Roeschlin M., Patani A., Kwiatkowska M., Martinovic I. Broken Hearted: How To Attack ECG Biometrics. In Proceedings of the NDSS, San Diego, CA, USA, 26 February – 1 March 2017. P. 1–15. Doi: http://dx.doi.org/10.14722/ndss.2017.23408.
13. Alves A., Carreiras C. CardioWheel: ECG Biometrics on the Steering Wheel. In Proceedings of the European Conference: Machine Learning and Knowledge Discovery in Databases, Porto, Portugal, 7–11 September 2015. P. 267–270.
14. Fatimah B., Singh P., Singhal A., Pachori R. B. Biometric Identification From ECG Signals Using Fourier Decomposition and Machine Learning. IEEE Trans. Instrum. Meas. 2022, 71, 1–9. Doi: https://doi.org/ 10.1109/TIM.2022.3199260.
15. Homer M., Irvine J. M., Wendelken S. A model-based approach to human identification using ECG. In Proceedings of the SPIE Conference on Optics and Photonics for Global Homeland Security V and Biometric Technology for Human Identification VI, Orlando, FL, USA, 13–17 April 2009. Volume 7306. 730625. Doi: http://dx.doi.org/10.1117/12.819327.
16. Benouis M., Mostefai L., Costen N., Regouid M. ECG-based biometric identification using one-dimensional local difference pattern. Biomed. Signal Process. Control. 2021, 64, 102226. Doi: https: //doi.org/10.1016/j.bspc.2020.102226.
17. Irvine J. M., Wiederhold B. K., Gavshon L. W., Israel S., McGehee S. B., Meyer R., Wiederhold M. D. Heart rate variability: A new biometric for human identification. In Proceedings of the International Conference on Artificial Intelligence (IC-AI'01), Las Vegas, NV, USA, 7–9 November 2001. P. 1106–1111.
18. Wan Y., Yao J. A neural network to identify human subjects with electrocardiogram signals. In Proceedings of the World Congress on Engineering and Computer Science, San Francisco, CA, USA, 22–24 October 2008. P. 1–4.
19. Chan A. D., Hamdy M. M., Badre A., Badee V. Wavelet distance measure for person identification using electrocardiograms. IEEE Trans. Instrum. Meas. 2008. 57. Р. 248–253. Doi: https://doi.org/10.1109/TIM. 2007.909996.
20. Ye C., Coimbra M. T., Kumar B. V. Investigation of human identification using two-lead electrocardiogram (ECG) signals. In Proceedings of the 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), Washington, DC, USA, 27–29 September 2010; P. 1–8. Doi: https://doi.org/10.1109/BTAS.2010.5634478.
21. García-González M. A., Argelagós-Palau A. Data from: combined measurement of ECG, breathing, and Seismocardiograms. PhysioNet. 2013. Doi: https://doi.org/10.13026/C2KW23.
22. García-González M. A., Argelagós-Palau A., Fernández-Chimeno M., Ramos-Castro J. A comparison of heartbeat detectors for the seismocardiogram. In Computing in Cardiology 2013, September 22–25, 2013. P. 461–464.
23. Goldberger A., Amaral L., Glass L., Hausdorff J., Ivanov P. C., Mark R. et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 2000. 101. e215–e220. Doi: https://doi.org/10.1161/01.cir.101.23.e215.
24. Lupenko S. The rhythm-adaptive Fourier series decompositions of cyclic numerical functions and one-dimensional probabilistic characteristics of cyclic random processes. Digital Signal Processing. 2023. 104104, ISSN 1051-2004. Doi: https://doi.org/10.1016/j.dsp.2023.104104.
25. Lupenko S. The Mathematical Model of Cyclic Signals in Dynamic Systems as a Cyclically Correlated Random Process. Mathematics 2022, 10, 3406. Doi: https://doi.org/10.3390/math10183406.
26. Lupenko S., Lytvynenko I., Sverstiuk A., Horkunenko A., Shelestovskyi B. Software for statistical processing and modeling of a set of synchronously registered cardio signals of different physical nature. CEUR Workshop Proceedings. 2021.2864. P. 194–205.
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