The technological advancements in the field of remote sensing have resulted in substantial growth of the telemedicine industry. While health care practitioners may now monitor their patients’ well-being from a distance and deliver their services remotely, the lack of physical presence introduces security risks, primarily with regard to the identity of the involved parties. The sensing apparatus, that a patient may employ at home, collects and transmits vital signals to medical centres which respond with treatment decisions despite the lack of solid authentication of the transmitter’s identity. In essence, remote monitoring increases the risks of identity fraud in health care. This paper proposes a biometric identification solution suitable for continuous monitoring environments. The system uses the electrocardiogram (ECG) signal in order to extract unique characteristics which allow to discriminate users. In security, ECG falls under the category of medical biometrics, a relatively young but promising field of biometric security solutions. In this work, the authors investigate the idiosyncratic properties of home telemonitoring that may affect the ECG signal and compromise security. The effects of psychological changes on the ECG waveform are taken into consideration for the design of a robust biometric system that can identify users based on cardiac signals despite physical or emotional variations. 1. Introduction For a number of severe diseases such as diabetes, hypertension, or respiratory disorders, where hospitalization might not always be justified or needed, home care is traditionally preferred. Moreover, the visit of a medical practitioner to the patient’s home is not necessarily an efficient solution either. This is because (i) the high health care costs for this service are undesirable; (ii) it may be infeasible for the personnel to reach highly rural areas; and (iii) monitoring is usually required on a continuous basis, rather than per visit. Home telemonitoring is now a reality, addressing the above problem very effectively, that is, it is not only cost-efficient but can also reach isolated communities and allow for 24？hr reporting on the patient’s status. Nevertheless, the widespread utilization of telemonitoring increases the risk of identity fraud in health care. Due to the lack of physical presence at the time of collection of the medical information (e.g., vital signals), the identity of the user that transmits the respective information is uncertain. Typically, every monitoring device is assigned with a unique ID (e.g., a serial code)
References
[1]
S. A. Israel, J. M. Irvine, A. Cheng, M. D. Wiederhold, and B. K. Wiederhold, “ECG to identify individuals,” Pattern Recognition, vol. 38, no. 1, pp. 133–142, 2005.
[2]
F. Agrafioti, F. M. Bui, and D. Hatzinakos, “Medical biometrics: the perils of ignoring time dependency,” in Proceedings of the IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems (BTAS '09), pp. 1–6, Washington, DC, USA, September 2009.
[3]
F. Agrafioti, F. M. Bui, and D. Hatzinakos, “Medical biometrics in mobile health monitoring,” Security and Communication Networks, vol. 4, no. 5, pp. 525–539, 2011.
[4]
G. Wübbeler, M. Stavridis, D. Kreiseler, R. D. Bousseljot, and C. Elster, “Verification of humans using the electrocardiogram,” Pattern Recognition Letters, vol. 28, no. 10, pp. 1172–1175, 2007.
[5]
C. Ye, M. T. Coimbra, and B. V. K. V. Kumar, “Investigation of human identification using two-lead Electrocardiogram (ECG) signals,” in Proceedings of the 4th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS '10), pp. 1–8, September 2010.
[6]
G. Kozmann, R. L. Lux, and L. S. Green, “Geometrical factors affecting the interindividual variability of the ECG and the VCG,” Journal of Electrocardiology, vol. 33, pp. 219–227, 2000.
[7]
R. Hoekema, G. J. H. Uijen, and A. Van Oosterom, “Geometrical aspects of the interindividual variability of multilead ECG recordings,” IEEE Transactions on Biomedical Engineering, vol. 48, no. 5, pp. 551–559, 2001.
[8]
H. W. Draper, C. J. Peffer, F. W. Stallmann, D. Littmann, and H. V. Pipberger, “The corrected orthogonal electrocardiogram and vectorcardiogram in 510 normal men (Frank lead system),” Circulation, vol. 30, pp. 853–864, 1964.
[9]
F. Agrafioti, D. Hatzinakos, and A. Anderson, “ECG pattern analysis for emotion detection,” IEEE Transactions on Affective Computing, vol. 3, pp. 102–115, 2012.
[10]
G. N. Yannakakis, J. Hallam, and H. H. Lund, “Entertainment capture through heart rate activity in physical interactive playgrounds,” User Modelling and User-Adapted Interaction, vol. 18, no. 1-2, pp. 207–243, 2008.
[11]
S. Tognetti, M. Garbarino, A. T. Bonanno, M. Matteucci, and A. Bonarini, “Enjoyment recognition from physiological data in a car racing game,” in Proceedings of the 3rd ACM Workshop on Affective Interaction in Natural Environments (AFFINE '10), pp. 3–8, October 2010.
[12]
L. Sornmo and P. Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications, Elsevier, 2005.
[13]
J. T. Catalano, Guide to ECG Analysis, J.B. Lippincott, Philadelphia, Pa, USA, 1993.
[14]
L. Biel, O. Pettersson, L. Philipson, and P. Wide, “ECG analysis: a new approach in human identification,” IEEE Transactions on Instrumentation and Measurement, vol. 50, no. 3, pp. 808–812, 2001.
[15]
M. Kyoso and A. Uchiyama, “Development of an ECG identification system,” in Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 4, pp. 3721–3723, October 2001.
[16]
T. W. Shen, W. J. Tompkins, and Y. H. Hu, “One-lead ECG for identity verification,” in Proceedings of the 2nd Conference of the IEEE Engineering in Medicine and Biology Society, pp. 62–63, October 2002.
[17]
R. Palaniappan and S. M. Krishnan, “Identifying individuals using ECG beats,” in Proceedings of the International Conference on Signal Processing and Communications (SPCOM '04), pp. 569–572, December 2004.
[18]
K. S. Kim, T. H. Yoon, J. W. Lee, D. J. Kim, and H. S. Koo, “A robust human identification by normalized time-domain features of electrocardiogram,” in Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society (IEEE-EMBS '05), pp. 1114–1117, September 2005.
[19]
S. Saechia, J. Koseeyaporn, and P. Wardkein, “Human identification system based ECG signal,” in Proceedings of the IEEE Region 10 Conference (TENCON '05), pp. 1–4, November 2005.
[20]
Z. Zhang and D. Wei, “A new ECG identification method using bayes' theorem,” in Proceedings of the IEEE Region 10 Conference (TENCON '06), pp. 1–4, November 2006.
[21]
Y. N. Singh and P. Gupta, “ECG to individual identification,” in Proceedings of IEEE International Conference on Biometrics: Theory, Applications and Systems, pp. 1–8, October 2008.
[22]
O. Boumbarov, Y. Velchev, and S. Sokolov, “ECG personal identification in subspaces using radial basis neural networks,” in Proceedings of the 5th IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS '09), pp. 446–451, September 2009.
[23]
C. M. Ting and S. H. Salleh, “ECG based personal identification using extended Kalman filter,” in Proceedings of the 10th International Conference on Information Sciences, Signal Processing and their Applications (ISSPA '10), pp. 774–777, May 2010.
[24]
M. M. Tawfik, H. Selim, and T. Kamal, “Human identification using time normalized QT signal and the QRS complex of the ECG,” in Proceedings of the 7th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP '10), pp. 755–759, July 2010.
[25]
K. N. Plataniotis, D. Hatzinakos, and J. K. M. Lee, “ECG biometric recognition without fiducial detection,” in Proceedings of the Biometrics Symposium, Baltimore, Md, USA, September 2006.
[26]
G. G. Molina, F. Bruekers, C. Presura, M. Damstra, and M. van der Veen, “Morphological synthesis of ECG signals for person authentication,” in Proceedings of 15th European Signal Processing Conference, Poland, 2007.
[27]
A. D. C. Chan, M. M. Hamdy, A. Badre, and V. Badee, “Wavelet distance measure for person identification using electrocardiograms,” IEEE Transactions on Instrumentation and Measurement, vol. 57, no. 2, pp. 248–253, 2008.
[28]
C. C. Chiu, C. M. Chuang, and C. Y. Hsu, “A novel personal identity verification approach using a discrete wavelet transform of the ECG signal,” in Proceedings of the International Conference on Multimedia and Ubiquitous Engineering (MUE '08), pp. 201–206, April 2008.
[29]
S. Z. Fatemian and D. Hatzinakos, “A new ECG feature extractor for biometric recognition,” in Proceedings of the 16th International Conference on Digital Signal Processing, pp. 1–6, July 2009.
[30]
I. Odinaka, P. H. Lai, A. D. Kaplan et al., “ECG biometrics: a robust short-time frequency analysis,” in 2010 IEEE International Workshop on Information Forensics and Security, WIFS 2010, pp. 1–6, December 2010.
[31]
D. P. Coutinho, A. L. N. Fred, and M. A. T. Figueiredo, “One-lead ECG-based personal identification using Ziv-Merhav cross parsing,” in Proceedings of the 20th International Conference on Pattern Recognition (ICPR '10), pp. 3858–3861, August 2010.
[32]
N. Ghofrani and R. Bostani, “Reliable features for an ECG-based biometric system,” in Proceedings of the 17th Iranian Conference in Biomedical Engineering (ICBME '10), pp. 1–5, November 2010.
[33]
M. Li and S. Narayanan, “Robust ECG biometrics by fusing temporal and cepstral information,” in Proceedings of the 20th International Conference on Pattern Recognition (ICPR '10), pp. 1326–1329, August 2010.
[34]
F. Agrafioti and D. Hatzinakos, “ECG based recognition using second order statistics,” in Proceedings of the 6th Annual Communication Networks and Services Research Conference (CNSR '08), pp. 82–87, Halifax, UK, May 2008.
[35]
F. Agrafioti and D. Hatzinakos, “Fusion of ECG sources for human identification,” in Proceedings of the 3rd International Symposium on Communications, Control, and Signal Processing (ISCCSP '08), pp. 1542–1547, March 2008.
[36]
R. W. Picard, E. Vyzas, and J. Healey, “Toward machine emotional intelligence: analysis of affective physiological state,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 10, pp. 1175–1191, 2001.
[37]
P. Ekman, R. W. Levenson, and W. V. Friesen, “Autonomic nervous system activity distinguishes among emotions,” Science, vol. 221, no. 4616, pp. 1208–1210, 1983.
[38]
F. H？nig, A. Batliner, and E. N？th, “Real-time Recognition of the Affective User State with Physiological Signals,” in Proceedings of the 2nd International Conference on Affective Computing and Intelligent Interaction, pp. 1–8, Roddy Cowie and Fiorella de Rosis, 2007.
[39]
J. Anttonen and V. Surakka, “Emotions and heart rate while sitting on a chair,” in Proceedings of the Technology, Safety, Community—Conference on Human Factors in Computing Systems, pp. 491–499, April 2005.
[40]
C. M. Jones and T. Troen, “Biometric valence and arousal recognition,” in Proceedings of the Australasian Computer-Human Interaction Conference (OZCHI '07), pp. 191–194, November 2007.
[41]
R. L. Mandryk, K. M. Inkpen, and T. W. Calvert, “Using psychophysiological techniques to measure user experience with entertainment technologies,” Behaviour and Information Technology, vol. 25, no. 2, pp. 141–158, 2006.
[42]
G. N. Yannakakis, H. P. Martínez, and A. Jhala, “Towards affective camera control in games,” User Modelling and User-Adapted Interaction, vol. 20, no. 4, pp. 313–340, 2010.
[43]
R. Cowie, E. Douglas-Cowie, S. Savvidou, E. McMahon, M. Sawey, and M. Schroder, “FEELTRACE: an instrument for recording perceived emotion in real time,” in Proceedings of the iSCA Workshop Speech and Emotion, pp. 19–24, 2000.
