On Real Time ECG Segmentation Algorithms for Biometric Applications

Filipe Canento, André Lourenço, Hugo Silva, Ana Fred


Recognizing an individual’s identity through the use of characteristics intrinsic to that subject is a biometric recognition problem with increasingly number of modalities and applications. Recently, the electrical activity of the heart (the Electrocardiogram or ECG) has been explored as an additional modality to recognize individuals. The ECG signal contains several features, which are unique to each individual. The preprocessing of the ECG signal and the feature extraction steps are crucial for biometric recognition to be successful. In fiducial approaches, this last step is accomplished by correctly detecting the heart beats, and performing their segmentation to extract the biometric templates afterwards. In this work, we present an overview of the different steps of an ECG biometric system, focusing on the evaluation and comparison of multiple real-time heart beat detection and ECG segmentation algorithms, and their application to biometric systems. An evaluation and comparison of the algorithms with annotated datasets (MITDB, NSTDB) is presented, and methods to combine them in order to improve performance are discussed.


  1. Biel, L., Petterson, O., Phillipson, L., and Wide, P. (2001). ECG analysis: A new approach in human identification. IEEE Trans Instrumentation and Measurement, 50(3):808-812.
  2. Chan, A. D. C., Hamdy, M. M., Badre, A., and Badee, V. (2008). Wavelet distance measure for person identification using electrocardiograms. IEEE Trans on Instrumentation and Measurement.
  3. Christov, I. I. (2004). Real time electrocardiogram QRS detection using combined adaptive threshold. Biomed Eng Online, 3(1).
  4. Chung, E. K. (2000). Pocketguide to ECG Diagnosis. Blackwell Publishing Professional.
  5. Coutinho, D. P., Fred, A. L. N., and Figueiredo, M. A. T. (2010). String matching approach for ECG-based biometrics. Pattern Recognition, 16th Portuguese Conference on.
  6. Engelse, W. A. H. and Zeelenberg, C. (1979). A single scan algorithm for QRS-detection and feature extraction. Computers in Cardiology, 6:37-42.
  7. Gamboa, H. (2008). Multi-Modal Behavioural Biometrics Based on HCI and Electrophysiology. PhD thesis, Universidadte Técnica de Lisboa, Instituto Superior Técnico.
  8. Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P., Mark, R., Mietus, J., Moody, G., Peng, C., and Stanley, H. (2000). Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals. Circulation 101.
  9. Hamilton, P. (2002). Open source ecg analysis. Computers in Cardiology.
  10. Israel, S., Irvine, J., Cheng, A., Wiederhold, M., and Wiederhold, B. (2005). ECG to identify individuals. Pattern Recognition, 38(1):133-142.
  11. Jain, A., Flynn, P., and Ross, A. (2007). Handbook of Biometrics. Springer.
  12. Jain, A. K., Ross, A., and Prabhakar, S. (2004). An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Techn., 14(1):4-20.
  13. Jain, A. K., Ross, A. A., and Nandakumar, K. (2011). Introduction to Biometrics. Springer, 2011 edition.
  14. Lourenc¸o, A., Silva, H., and Fred, A. (2011). Unveiling the biometric potential of Finger-Based ECG signals. Computational Intelligence and Neuroscience, 2011.
  15. Loureno, A., Silva, H., Leite, P., Loureno, R., and Fred, A. L. N. (2012). Real time electrocardiogram segmentation for finger based ecg biometrics. In Huffel, S. V., Correia, C. M. B. A., Fred, A. L. N., and Gamboa, H., editors, BIOSIGNALS, pages 49-54. SciTePress.
  16. Moody, G. and Mark, R. (2001). The impact of the mit-bih arrhythmia database. IEEE Eng in Med and Biol.
  17. Moody, G., Muldrow, W., and Mark, R. (1984). A noise stress test for arrhythmia detectors. Computers in Cardiology.
  18. Oppenheim, A. V. and Schafer, R. W. (1975). Digital Signal Processing. Prentice Hall.
  19. Shen, T. (2005). Biometric Identity Verification Based on Electrocardiogram. PhD thesis, University of Wisconsin.
  20. Silva, H., Fred, A., and Lourenc¸o, A. (2011a). Check your biosignals here: Experiments on affective computing and behavioral biometrics. In Proc Portuguese Conf. on Pattern Recognition - RecPad, Porto, Portugal.
  21. Silva, H., Lourenc¸o, A., Lourenc¸o, R., Leite, P., Coutinho, D., and Fred, A. (2011b). Study and evaluation of a single differential sensor design based on electrotextile electrodes for ECG biometrics applications. In Proc IEEE Sensors, Limerick, Ireland.
  22. Wang, Y., Agrafioti, F., Hatzinakos, D., and Plataniotis, K. N. (2008). Analysis of human electrocardiogram for biometric recognition. EURASIP J. Adv. Signal Process, 2008:19.
  23. Zong, W., Heldt, T., Moody, G., and Mark, R. (2003). An open-source algorithm to detect onset of arterial blood pressure pulses. Computers in Cardiology, 30:259- 262.

Paper Citation

in Harvard Style

Canento F., Lourenço A., Silva H. and Fred A. (2013). On Real Time ECG Segmentation Algorithms for Biometric Applications . 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 228-235. DOI: 10.5220/0004245902280235

in Bibtex Style

author={Filipe Canento and André Lourenço and Hugo Silva and Ana Fred},
title={On Real Time ECG Segmentation Algorithms for Biometric Applications},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)},

in EndNote Style

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)
TI - On Real Time ECG Segmentation Algorithms for Biometric Applications
SN - 978-989-8565-36-5
AU - Canento F.
AU - Lourenço A.
AU - Silva H.
AU - Fred A.
PY - 2013
SP - 228
EP - 235
DO - 10.5220/0004245902280235