Eigen Heartbeats for User Identification

Marta S. Santos, Ana L. Fred, Hugo Silva, André Lourenço

Abstract

Electrocardiographic (ECG) signals record the heart’s electrical activity over time. These signals have typically been assessed for clinical purposes providing a fair evaluation of the heart’s condition. However, it has been shown recently that they also convey distinctive information that can be used for user identification. In this paper we explore these signals for user identification purposes, proposing two data representation and processing techniques based on principal component analysis (PCA) and classification based on the K-NN rule. We analyze and compare these techniques, showing experimentally that 100% identification rates can be achieved. The analysis covers an outlier removal procedure and different configurations of algorithmic and proposed system parameters.

References

  1. Agrafioti, Foteini (2011). PhD thesis. Univ. of Toronto.
  2. Biel, L.; Pettersson, O.; Philipson, L. and Wide, P. (2001) ECG analysis - a new approach in human identification. IEEE Transactions on Instrumentation and Measurement, 50(3):808-812.
  3. Chan, A.; Hamdy, M.; Badre, A. and Badee, V. (2008). Wavelet distance measure for person identification using electrocardiograms. IEEE Transactions on Instrumentation and Measurement, 57(2):248-253,
  4. Engelse, W. A. H.; and Zeelenberg, C. (1979). “A single scan algorithm for QRS-detection and feature extraction,” Comp.in Card., vol. 6, pp. 37-42.
  5. Irvine, J.; A., S. (2009). eigenPulse: Robust Human Identification from Cardiovascular Function. The Draper Technology Digest , pp. 50-59.
  6. Israel, S., M., J. (2010). The Heartbeat: the Living Biometric. Chapter in Biometrics: theory, Methods and Applications, pp. 429-459. Wiley
  7. Jain, A. and Flynn, P. and Ross, A. (2008). Handbook of Biometrics. Springer.
  8. Lourenço, A.; Silva, H.; Fred, A. (2011). Unveiling the Biometric Potential of Finger-Based ECG Signals. Computational Intelligence and Neuroscience
  9. Lourenço, A.; Silva, H.; Leite, P.; Lourenco, R., and Fred, A. (2011). “Real time electrocardiogram segmentation for finger based ECG biometric,” in Proc. of BIOSIGNALS 2012, pp. 49-54.
  10. Silva, H., Fred, A. L., and Lourenço, A. (2011). Check Your Biosignals Here: Experiments on Affective Computing and Behavioral Biometrics. RecPad.
  11. Silva, H.; Gamboa, H. and Fred, A. (2007). One lead ECG based personal identification with feature subspace ensembles. In Proc. of the MLDM 7807, pp. 770-783.
Download


Paper Citation


in Harvard Style

Santos M., Fred A., Silva H. and Lourenço A. (2013). Eigen Heartbeats for User Identification . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013) ISBN 978-989-8565-36-5, pages 351-355. DOI: 10.5220/0004249503510355


in Bibtex Style

@conference{biosignals13,
author={Marta S. Santos and Ana L. Fred and Hugo Silva and André Lourenço},
title={Eigen Heartbeats for User Identification},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)},
year={2013},
pages={351-355},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004249503510355},
isbn={978-989-8565-36-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)
TI - Eigen Heartbeats for User Identification
SN - 978-989-8565-36-5
AU - Santos M.
AU - Fred A.
AU - Silva H.
AU - Lourenço A.
PY - 2013
SP - 351
EP - 355
DO - 10.5220/0004249503510355