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Feasibility of Using Similar Electrocardiography Measured around the Ears to Develop a Personal Authentication System

귀 주변에서 측정한 유사 심전도 기반 개인 인증 시스템 개발 가능성

  • Choi, Ga-Young (Kumoh National Institute of Technology, Medical IT Convergence Engineering) ;
  • Park, Jong-Yoon (Kumoh National Institute of Technology, Medical IT Convergence Engineering) ;
  • Kim, Da-Yeong (Kumoh National Institute of Technology, Medical IT Convergence Engineering) ;
  • Kim, Yeonu (Kumoh National Institute of Technology, Medical IT Convergence Engineering) ;
  • Lim, Ji-Heon (Kumoh National Institute of Technology, Medical IT Convergence Engineering) ;
  • Hwang, Han-Jeong (Kumoh National Institute of Technology, Medical IT Convergence Engineering)
  • 최가영 (금오공과대학교 메디컬IT융합공학과) ;
  • 박종윤 (금오공과대학교 메디컬IT융합공학과) ;
  • 김다영 (금오공과대학교 메디컬IT융합공학과) ;
  • 김연우 (금오공과대학교 메디컬IT융합공학과) ;
  • 임지헌 (금오공과대학교 메디컬IT융합공학과) ;
  • 황한정 (금오공과대학교 메디컬IT융합공학과)
  • Received : 2019.12.17
  • Accepted : 2020.02.19
  • Published : 2020.02.29

Abstract

A personal authentication system based on biosignals has received increasing attention due to its relatively high security as compared to traditional authentication systems based on a key and password. Electrocardiography (ECG) measured from the chest or wrist is one of the widely used biosignals to develop a personal authentication system. In this study, we investigated the feasibility of using similar ECG measured behind the ears to develop a personal authentication system. To this end, similar ECGs were measured from thirty subjects using a pair of three electrodes attached behind each of the ears during resting state during which the standard Lead-I ECG was also simultaneously measured from both wrists as baseline ECG. The three ECG components, Q, R, and S, were extracted for each subject as classification features, and authentication accuracy was estimated using support vector machine (SVM) based on a 5×5-fold cross-validation. The mean authentication accuracies of Lead I-ECG and similar ECG were 90.41 ± 8.26% and 81.15 ± 7.54%, respectively. Considering a chance level of 3.33% (=1/30), the mean authentication performance of similar ECG could demonstrate the feasibility of using similar ECG measured behind the ears on the development of a personal authentication system.

Keywords

References

  1. Jain AK, Ross A, Prabhakar A. An introduction to biometric recognition. IEEE Trans Circuits Syst Video Technol. 2014;14:4-20. https://doi.org/10.1109/TCSVT.2003.818349
  2. Isenor DK, Zaky SG. Fingerprint identification using graph matching. Pattern Recognit. 1986;19(2):113-22. https://doi.org/10.1016/0031-3203(86)90017-8
  3. Hrechak AK, McHugh JA. Automated fingerprint recognition using structural matching. Pattern Recognit. 1990;23(8):893-904. https://doi.org/10.1016/0031-3203(90)90134-7
  4. Ma L, Tan T, Wang Y, Zhang D. Personal identification based on iris texture analysis. IEEE Trans Pattern Anal Mach Intell. 2003;25(12):1519-33. https://doi.org/10.1109/TPAMI.2003.1251145
  5. Ahonen T, Hadid A, Pietikainen M. Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell. 2016;12:2037-41.
  6. Rajagopal G, Manoharan SK. Personal authentication using multifeatures multispectral palm print traits. Sci. 2015;2015:1-12.
  7. Klonowski M, Plata M, Syga P. User authorization based on hand geometry without special equipment. Pattern Recognit. 2018;73:189-201. https://doi.org/10.1016/j.patcog.2017.08.017
  8. Sun, Y, Chen Y, Wang X, Tang X. Deep learning face representation by joint identification-verification. Adv Neural Inf Process Syst. 2014;1988-96.
  9. Haware S, Barhatte A. Retina based biometric identification using SURF and ORB feature descriptors. Conf Proc In 2017 International conference on Microelectronic Devices, Circuits and Systems (ICMDCS). 2017;1-6.
  10. Shaheed K, Liu H, Yang G, Qureshi I, Gou J, Yin Y (2018). A systematic review of finger vein recognition techniques. Information. 2018;9(9):213. https://doi.org/10.3390/info9090213
  11. Chen CH, Chu CT. High performance iris recognition based on 1-D circular feature extraction and PSO-PNN classifier. Expert Syst Appl. 2009;36(7):10351-6. https://doi.org/10.1016/j.eswa.2009.01.033
  12. Rai H, Yadav A. Iris recognition using combined support vector machine and Hamming distance approach. Expert Syst Appl. 2014;41(2):588-93. https://doi.org/10.1016/j.eswa.2013.07.083
  13. Biel L, Pettersson O, Philipson L, Wide P. ECG analysis: a new approach in human identification. IEEE Trans Instrum Meas. 2001;50(3):808-12. https://doi.org/10.1109/19.930458
  14. Shen TWD, Tompkins WJ, Hu YH. Implementation of a onelead ECG human identification system on a normal population. J Eng Comput Innov. 2010;2(1):12-21.
  15. Kang SJ, Lee SY, Cho HI, Park H. ECG authentication system design based on signal analysis in mobile and wearable devices. IEEE Signal Process Lett. 2016;23(6):805-8. https://doi.org/10.1109/LSP.2016.2531996
  16. Cao K, Jain AK. Hacking mobile phones using 2D printed fingerprints. Michigan State University Tech Report MSUCSE-16-2. 2016:1-3.
  17. Erdogmus N, Marcel S. Spoofing face recognition with 3D masks. IEEE Trans Inf Forensic Secur. 2014;9(7):1084-97. https://doi.org/10.1109/TIFS.2014.2322255
  18. Gradl S, Kugler P, Lohmuller C, Eskofier B. Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices. Conf Proc In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2012;2452-5.
  19. Crawford J, Doherty L. Ten steps to recording a standard 12-lead ECG. Practice Nursing. 2010;21(12): 622-30. https://doi.org/10.12968/pnur.2010.21.12.622
  20. Zhang Q, Zeng X, Hu W, Zhou D. A machine learning-empowered system for long-term motion-tolerant wearable monitoring of blood pressure and heart rate with Ear-ECG/PPG. IEEE Access. 2017;5:10547-61. https://doi.org/10.1109/ACCESS.2017.2707472
  21. Da He D, Winokur ES, Sodini CG. An ear-worn vital signs monitor. IEEE Trans Biomed Eng. 2015;62(11):2547-52. https://doi.org/10.1109/TBME.2015.2459061
  22. Wubbeler G, Stavridis M, Kreiseler D, Bousseljot RD, Elster C. Verification of humans using the electrocardiogram. Pattern Recognit Lett. 2007;28(10):1172-5. https://doi.org/10.1016/j.patrec.2007.01.014
  23. Israel SA, Irvine JM, Cheng A, Wiederhold MD, Wiederhold BK. ECG to identify individuals. Pattern Recognit. 2005;38(1):133-42. https://doi.org/10.1016/j.patcog.2004.05.014
  24. Chan AD, Hamdy MM, Badre A, Badee V. Wavelet distance measure for person identification using electrocardiograms. IEEE Trans Instrum Meas. 2008;57(2):248-53. https://doi.org/10.1109/TIM.2007.909996
  25. Osselton JW. Acquisition of EEG data by bipolar unipolar and average reference methods: a theoretical comparison. Electroencephalogr. Clin Neurophysiol. 1965;19:527-8. https://doi.org/10.1016/0013-4694(65)90195-1
  26. Wang Y, Agrafioti F, Hatzinakos D, Plataniotis KN. Analysis of human electrocardiogram for biometric recognition. Conf Proc EURASIP journal on Advances in Signal Processing, 2008;1: 148658.
  27. Shen TW, Tompkins WJ, Hu YH. One-lead ECG for identity verification. Conf Proc In the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society. 2002;1:62-63.
  28. Hassan Z, Gilani SO, Jamil M. Review of fiducial and nonfiducial techniques of feature extraction in ECG based biometric systems. Indian J Sci Technol. 2016;9(21):23-6.
  29. Sun Y, Chan KL, Krishnan SM. Characteristic wave detection in ECG signal using morphological transform. BMC Cardiovasc Disord. 2005;5(1):28. https://doi.org/10.1186/1471-2261-5-28
  30. O'Haver T. Command-line findpeaks MATLAB function.
  31. Ubeyli ED. ECG beats classification using multiclass support vector machines with error correcting output codes. Digit Signal Prog. 2007;17(3):675-84. https://doi.org/10.1016/j.dsp.2006.11.009
  32. Acir N. A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems. Expert Syst Appl. 2006;31(1):150-8. https://doi.org/10.1016/j.eswa.2005.09.013
  33. Saini I, Singh D, Khosla A. Electrocardiogram beat classification using empirical mode decomposition and multiclass directed acyclic graph support vector machine. Comput Electr Eng. 2014;40(5):1774-87. https://doi.org/10.1016/j.compeleceng.2014.04.004