Abstract
A new biometric recognition material electrocardiogram (ECG) waveform was developed rapidly in recent ten years. Except the common feature of biometric recognition, its unique “aliveness” and individual difference of heart geometric structure, This make ECG waveform has becoming a kind of high security level biometric. The paper proposed a similarity measurement strategy to do recognition work by ECG waveform. It uses the ECG waveforms collected from one person as his or her ECG waveform sample set. These ECG waveforms were partitioned into single ECG waveform (which is generate by one heart beat) firstly. And the discrete points that formed single ECG waveforms were overlaid into a two dimension coordinate. The points which have same coordinate will be accumulated, and the color of this coordinate will be changed into a 8 bit color system according to the overlay number of point. After the procedures, it presents a hierarchy color changing image, which can be used as a tunnel like ECG morph. Moreover, the inclusive degree can be computed by color clustering status. In the end, the morph will become the morph model of the person to judge the belonging of new ECG data. The ECG data used in the paper is from MIT/BIH ECG standard data set. From the results, the recognition accurate of ECG recognition can reach to 95.1% averagely.
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References
Hoekema, R., Uijen, G.J.H., van Oosterom, A.: Geometrical aspect of the interindividual variability of multilead ECG recordings. IEEE Trans. Biomedical. Engineering 48, 551–559 (2001)
Simon, B.P., Eswaran, C.: An EGG classifier designed using modified decision based neural network. Computer and Biomedical Research 30, 257–272 (1997)
Biel, L., Patterson, O., Philipson, L., Wide, P.: ECG analysis: a new approach in human identification. In: Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference, Venice, Italy, pp. 557–561 (1999); and IEEE Transactions on Instrumentation and Measurement, pp.808-812 (2001)
Israel, S.A., Irvine, J.M., Cheng, A., Wiederhold, M.D., Wiederhold, B.K.: ECG to Identify Individuals. Pattern Recognition 38(1), 138–142 (2005)
Irvine, J.M., Wiederhold, B.K., Gavshon, L.W., Israel, S.A., McGehee, S.B., Meyer, R., Wiederhold, M.D.: Heart Rate Variability: A New Biometric for Human Identification. In: International Conference on Artificial Intelligence (ICAI 2001), Las Vegas, Nevada, vol. III, pp. 1106–1111 (2001)
Irvine, J.M., Israel, S.A., Wiederhold, M.D., Wiederhold, B.K.: A New Biometric: Human Identification from Circulatory Function. Joint Statistical Meetings of the American Statistical Association, SanFrancisco, 7 pages (2003)
Israel, S.A., Irvine, J.M., Wiederhold, B.K., Wiederhold, M.D.: The Heartbeat: The Living Biometric. In: Boulgouris, N.V., Micheli-Tzanakou, E., Plataniotis, K.N. (eds.) Biometrics: Theory, Methods, and Applications. John Wiley and Sons/IEEE (March 2010)
Ogawa, M., et al.: Fully Automated Biosignal Acquisition System for Home Health Monitoring. In: Proc. of the 19th IEEE EMBS Intl. Conf., Chicago, USA (1997)
Kyoso, M., Uchiyama, A.: Developrnent of an ECG Identification System. In: Proc. of the 23th IEEE EMBS Intl. Conf., Istanbul, Turekey (2001)
Shen, T.W., Tompkinsl, W.J., Hu, Y.H.: One-Lead ECG For Identity Verification. In: Proceedings 2001 2nd Joint Conference of the IEEE Engineering in Medicine and Biology Society and the Biomedical Engineering Society, EMBSBMES, Houston, TX, USA, October 23-26 (2002)
Kim, K.-S., Yoon, T.-H., Lee, J.-W., Kim, D.-J., Koo, H.-S.: A Robust Human Identification by Normalized Time-Domain Features of Electrocardiogram. In: Proceedings of the 27th Annual Conference on 2005 IEEE Engineering in Medicine and Biology, Shanghai, China, September 1-4 (2005)
Zhang, Z., Wei, D.: A new ECG identificatiom mothed using bayes’ theorem
Irvine, J.M., Israel, S.A., Scruggs, W.T., Worek, W.J.: eigenPulse: Robust Human Identification from Cardiovascular Function. Pattern Recognition 41, 3427–3435 (2008)
Israel, S.A., Scruggs, W.T., Worek, W.J., Irvine, J.M.: Fusing face and ECG for personal identification. In: Proceedings of 32nd Applied Imagery Pattern Recognition Workshop, vol. 1, pp. 226–231 (2003)
Wang, Y., Plataniotis, K.N., Hatzinakos, D.: Integrating Analytic And Appearance Attributes For Human Identification From Ecg Signals. In: Proceedings of Biometrics Symposiums (BSYM), Baltimore (September 2006)
Gahi, Y., Amrani, M., Zoglat, A., Guennoun, M., Kapralos, B., El-Khatib, K.: Biometric Identification System Based on Electrocardiogram Data. In: 2nd IEEE International Conference on New Technologies, Mobility and Security (NMTS 2008), Tangier, Morocco, November 5-7 (2008)
Chan, A. D. C., Hamdy, M. M., Badre, A., Badee, V.: Wavelet Distance Measure for Person Identification Using Electrocardiograms. IEEE Transactions on Instrumentation and Measurement 57(2) (February 2008)
Plataniotis, K.N., Hatzinakos, D., Lee, J.K.M.: Ecg Biometric Recognition Without Fiducial Detection. In: Biometrics Symposium (2006)
Agrafioti, F., Hatzinakos, D.: Fusion of ECG sources for human identification. In: ISCCSP (2008)
Zahra Fatemian, S., Hatzinakos, D.: A New Ecg Feature Extractor For Biometric Recognition. IEEE (2009)
Agrafioti, F., Hatzinakos, D.: Signal validation for cardiac biometrics. In: Proceedings of IEEE International Conference on Acoustics Speech and Signal Processing, Dallas, TX, vol. 1, pp. 1734–1737 (2010)
Ye, C., Coimbra, M.T., Vijaya Kumar, B.V.K.: Investigation of Human Identification using. Two-Lead Electrocardiogram (ECG) Signals
Chiu, C.-C., Chuang, C.-M., Hsu, C.-Y.: A Novel Personal Identity Verification Approach Using a Discrete Wavelet Transform of the ECG Signal. In: Proceedings of International Conference on Multimedia and Ubiquitous Engineering, Busan,Korea, vol. 1, pp. 201–206 (2008)
Shi, J., Lam, K.-Y.: VitaCode: Electrocardiogram Representation for Biometric Cryptography in Body Area Networks
Li, Z., Yuan, J., Yang, H.: Analysis distances for similarity estimation by Fuzzy C-Mean algorithm. In: The Eighth International Conference on Machine Learning and Cybernetics, Baoding, China, vol. (1), pp. 582–587 (2009)
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Zheng, G., Li, ZY., Liu, TT., Dai, M. (2011). Study of Human Identification by Electrocardiogram Waveform Morph. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds) Biometric Recognition. CCBR 2011. Lecture Notes in Computer Science, vol 7098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25449-9_34
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DOI: https://doi.org/10.1007/978-3-642-25449-9_34
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