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An integration of features for person identification based on the PQRST fragments of ECG signals

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Abstract

In this document, the aim of this study is to identify the subjects using PQRST fragments of the electrocardiogram (ECG) signal. The ECG signal is an emerging technology for person identification. Our identification system has three principal steps, namely preprocessing, features extraction, and classification. In the first step, the filtering technique is used to remove the noise of the ECG signal. After filtering, the algorithm of T peaks detection is implemented for realizing the segmentation. (This work focuses on the PQRST fragments.) In the second step, a combination of the features such as cepstral coefficients, entropy, and zero crossing rate is proposed in this work. After feature extraction step, the machine learning model like the support vector machines is used for the classification step. A combination of the different features is evaluated using two public databases such as ECG-ID database and Massachusetts Institute of Technology–Boston’s Beth Israel Hospital Arrhythmia DataBase obtained from the Physionet database. Our proposed system gives an accuracy rate of 92.5% with ECG-ID database (all-recordings) and 98.6% with MIT–BIHA database.

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  1. https://archive.physionet.org/cgi-bin/atm/ATM.

References

  1. Houalef, S., Bendahmane, A., Benyettou, A.: Système de reconnaissance biométrique multimodal basé sur la fusion: empreinte digitale, visage, géométrie de la main. Departement d’informatique, pp. 1–7 (2012)

  2. Jin, S.K., Sung, B.P.: A study on emg-based biometrics. J. Internet Serv. Inf. Secur. 7, 19–31 (2017)

    Google Scholar 

  3. Kalaivani, S., Damon, L.W.: Deep Learning for Biometrics: A Survey. ACM Computing Surveys (CSUR) 51, 1–34 (2018)

    Google Scholar 

  4. Florent, P., Jean, L.D.: Introduction à la Biométrie Authentification des Individus par Traitement Audio-Vidéo. Traitement du Signal 19, 253–265 (2007)

    Google Scholar 

  5. Parvinder, S.S., Iqbaldeep, K., Amit, V., Samriti, J., Shailendra, S.: Biometric Methods and Implementation of Algorithms. International Journal of Computer, Electrical, Automation, Control and Information Engineering 3, 1033–1038 (2009)

    Google Scholar 

  6. Richa, S., Mayank, V., Phalguni, G.: Biometrics encyclopedia of multimedia technology and networking, 2nd edn., pp. 121–127 (2009)

  7. Samer, C., Amine, N.A., Patrick, K., Mohamad, K.: ECG modelling using wavelet networks: application to biometrics. IEEE Transactions on Information Technology in Biomedicine 2, 236–249 (2010)

    Google Scholar 

  8. Anwar, E.I.: ECG signals for human identification based on fiducial and non-fiducial approaches. International Journal of Advanced Computer Research 10, 2277–7970 (2020)

    Google Scholar 

  9. Islam M.I., Haikel, H.: HBS: a novel biometric feature based on heartbeat morphology. IEEE Trans. Inf. Technol. Biomed. 16, 445–453 (2012)

    Article  Google Scholar 

  10. Ingale, M., Cordeiro, R., Thentu, S.: Ecg biometric authentication: A comparative analysis. IEEE Access 8, 117853–117866 (2020)

    Article  Google Scholar 

  11. Chang, C.: Authentication biométrique par dynamique de frappe pour évaluationà distance utilisant svmà une classe (2016)

  12. Sihem, H., Yassine, B.A.: Svm for human identification using the ECG signal. Intell. Procedia Comput. Sci. 176, 430–439 (2020)

    Article  Google Scholar 

  13. Dalal, A.A., Islam, M.D.S.: Using Convolutional Neural Network and a Single Heartbeat for ECG Biometric Recognition. Entropy 23, 733 (2021)

    Article  Google Scholar 

  14. Mohamed, H., Gongning, L., Kuanquan, W.: Cancelable biometric authentication system based on ECG. Multimedia Tools Appl. 78, 1857–1887 (2019)

    Article  Google Scholar 

  15. Hugo, P.S., Andre, L., Fred, L.N., Ana, F.C.: ECG Biometrics: Principles and applications. In: Proceedings of the International Conference on Bioinspired Systems and Signal Processing, pp. 215–220 (2013)

  16. Matta, R., Lau, J.K.H., Agrafioti, F., Hatzinakos, D.: Real-time continuous identification system using ECG signals. In: 24th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 001313–001316 (2011)

  17. Fen M., Shu-Di B., Ye L.: Physiological Signal Based Biometrics for Securing Body Sensor Network. InTech (2012)

  18. Alotaiby, T.N., Alshebeili, S.A., Aljafar, L.M., Alsabhan, W.M.: ECG-based subject identification using common spatial pattern and SVM. Journal of Sensors 2019, 1–9 (2019)

    Article  Google Scholar 

  19. Nawal, M., Purohit, G.N.: ECG based human authentication: a review. International Journal of Emerging Engineering Research and Technology 2, 178–185 (2014)

    Google Scholar 

  20. Tantawi, M.M., Revett, K., Salem, A., Tolba, M.F.: Fiducial feature reduction analysis for electrocardiogram (ECG) based biometric recognition. Journal of Intelligent Information Systems 40, 17–39 (2013)

    Article  Google Scholar 

  21. Pirbhulal, S., Zhang, H., Mukhopadhyay, S. C., Li, C., Wang, Y., Li, G., et al.: An efficient biometric-based algorithm using heart rate variability for securing body sensor networks. Sensors 15, 15067–15089 (2015)

    Article  Google Scholar 

  22. Peter, S., Pratap Reddy, B., Momtaz, F., Givargis, T.: Design of secure ECG-based biometric authentication in body area sensor networks. Sensors 16, 570 (2016)

    Article  Google Scholar 

  23. Pinto, J. R., Cardoso, J. S., Lourenço, A., Carreiras, C.: Towards a continuous biometric system based on ECG signals acquired on the steering wheel. Sensors 17, 2228 (2017)

    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: Proceedings of the 2015 5th International Conference on IT Convergence and Security (ICITCS), Kuala Lumpur, Malaysia, pp. 1–5 (2015)

  25. Sarkar, A., Abbott, A.L., Doerzaph, Z.: ECG Biometric Authentication using a dynamical model. In: Proceedings of the 7th International Conference on Biometric Theory, Applications and Systems (BTAS), Arlington, VA, USA, pp. 1–6 (2015)

  26. 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: Proceedings of the 2015 2nd International Conference on Information Security and Cyber Forensics (InfoSec 2015), Cape Town, South Africa, pp. 5–10 (2015)

  27. Zhang, Q., Dian, Z., Xuan, Z.: HeartID: a multiresolution convolutional neural network for ECG-based biometric human identification in smart health applications. IEEE Access 5, 11805–11816 (2017)

  28. Altan, G., Kutlu, Y., Yeniad, M.: ECG based human identification using Second Order Difference Plots. Comput. Methods Programs Biomed. 170, 81–93 (2019)

    Article  Google Scholar 

  29. Di, W., Yujuan, S., Weiyi, Y., Gong, Z., and Jia, L.: A novel electrocardiogram biometric identification method based on temporal-frequency autoencoding. Electronics 8, 667 (2019)

    Article  Google Scholar 

  30. Tan, R., Marek, P.: Toward improving electrocardiogram (ECG) biometric verification using mobile sensors: a two stage classifier approach. Sensors 17, 410 (2017)

    Article  Google Scholar 

  31. Sedghamiz, H.: BioSigKit: a Matlab toolbox and interface for analysis of BioSignals. J. Open Source Softw. 3, 671 (2018)

    Article  Google Scholar 

  32. Noureddine, B., Fethi, B.R., Amine, N.A., Régis, F.: Person identification system based on electrocardiogram signal using labview. International Journal on Computer Science and Engineering 4, 974 (2012)

    Google Scholar 

  33. Nicolas, O., Marco, L.: On the generalization of Shannon entropy for speech recognition. In: IEEE Spoken Language Technology Workshop (SLT), Miami, FL, USA, pp. 97–102 (2012)

  34. Furui, S.: Speaker recognition in smart environments. In: Human-Centric Interfaces for Ambient Intelligence, pp. 163–184 (2010)

  35. Toh, A.M., Togneri, R., Nordholm, S.: Spectral entropy as speech features for speech recognition. Proceedings of PEECS 1, 92 (2005)

    Google Scholar 

  36. Chen, Y., Wan, C.Y., Lee, L. S.: Entropy-based feature parameter weighting for robust speech recognition. In: 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, pp. I–I (2006)

  37. Shannon, C.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)

    Article  MathSciNet  Google Scholar 

  38. Theodoros, G., Aggelos, P.: Introduction to Audio Analysis. In: A MATLAB approach, Academic Press, pp. 59–103 (2014)

  39. Mohammed, J.A.D., Abbas, E.M., Ahmed, F.: Speech emotion recognition based on SVM and KNN classifications fusion. Int. J. Electr. Comput. Eng. (IJECE) 11, 1259–1264 (2021)

  40. Hasan M., Boris F. SVM: Machinesa vecteurs de support ou séparateursa vastes marges. Rapport technique, Versailles St Quentin, France, Cité, 64 (2006)

  41. Sihem, H., Yassine, B.A.: Biometric individual identification system based on the ECG signal. Intelligent Systems Design and Applications 72, 416–425 (2020)

    Google Scholar 

  42. Khan, M.U., Aziz, S., Sohail, M., Naqvi, S.Z.H., Samer, S., Sajid, Z.: Detection of subacute intestinal obstruction from surface electromyography signatures. In: International Conference on Emerging Trends in Smart Technologies (ICETST), pp. 1–6 (2020)

  43. Khan, M.U., Aziz, S., Malik, A., Imtiaz, M.A.: Detection of myocardial infarction using pulse plethysmograph signals. In: International Conference on Frontiers of Information Technology (FIT), pp. 95–955 (2019)

  44. Khan, M.U., Aziz, S., Sohail, M., Shahid, A.A., Samer, S.: Automated detection and classification of gastrointestinal diseases using surface-EMG signals. In: 22nd International Multitopic Conference (INMIC), pp. 1–8 (2019)

  45. Sahin, I., Okan, K., Ergin, S.: Biometric person authentication framework using polynomial curve fitting based ECG feature extraction. Journal of Electrical Engineering & Computer Sciences 27, 3682–3698 (2019)

    Google Scholar 

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  1. Multimedia InfoRmation systems and Advanced Computing Laboratory, MIRACL University of Sfax, Sfax, Tunisia

    Sihem Hamza & Yassine Ben Ayed

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  1. Sihem Hamza
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  2. Yassine Ben Ayed
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Hamza, S., Ayed, Y.B. An integration of features for person identification based on the PQRST fragments of ECG signals. SIViP 16, 2037–2043 (2022). https://doi.org/10.1007/s11760-022-02165-8

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  • DOI: https://doi.org/10.1007/s11760-022-02165-8

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