Detection of Sharp Wave Activity in Biological Signals using Differentiation between Consecutive Samples

José L. Ferreira, Pierre J. M. Cluitmans, Ronald M. Aarts

Abstract

A number of signal processing techniques make use of first-derivative-based approaches for detecting regions of interest in biological signals. For instance, central and five-point derivative-based algorithms are employed for emphasizing and identification of the QRS complex in the ECG signal. Signal differentiation approaches are also used for detection and removal of high-frequency components associated to artefacts in the EEG signal. This paper aims to present a first-derivative approach based upon differentiation of consecutive samples – signal slope adaption (SSD) – for detecting regions of sharp wave activity in biological signals. A case study is analysed whereby SSD is used to mark and select the sharp wave activity associated to the QRS complex in the electrocardiogram. Evaluation of our methodology reveals that SSD shows to be effective for identification of QRS samples and, thereby, could be also employed to detect samples associated to sharp wave activity regions of other biological signals which possess similar signal slope behaviour.

References

  1. Allen, P., Polizzi, G., Krakow, K., Fish, D., Lemieux, L., 1998. Identification of EEG events in the MR scanner: the problem of pulse artefact and a method for its subtraction. NeuroImage. 8, 229-239.
  2. ANSI/AAMI/ISO EC57, 1998. Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms. AAMI Recommended Practice/ American National Standard.
  3. Arzeno, N., Deng, Z., Poon, C., 2008. Analysis of firstderivative based QRS detection algorithms. IEEE Trans. Biomed. Eng. 55 (2), 478-484.
  4. Benitez, D., Gaydecki, P., Zaidi, A., Fitzpatrick, A., 2000. A new QRS detection algorithm based on the Hilbert transform. Comput. Cardiol. 27, 379-382.
  5. Clifford, G., 2006. ECG statistics, noise, artifacts, and missing data. In G. Clifford, F. Azuaje, P. McSharry, (eds.), Advanced tools for ECG data analysis. Artech House: Boston, London.
  6. Cluitmans, P., Jansen, J., Beneken, J., 1993. Artefact detection and removal during auditory evoked potential monitoring. J. Clin. Monit. 9 (2), 112-120.
  7. Ferreira, J., Cluitmans, P., Aarts, R. M., 2012. Gradient artefact correction in the EEG signal recorded within the fMRI scanner. Proceedings of the 5th International Conference on Bio-inspired Systems and Signal Processing, BIOSIGNALS 2012, Vilamoura, Portugal, February 1 - 4, 2012. 110-117.
  8. Hamilton, P., Tompkins, W., 1986. Quantitative investigation of QRS detection rules using the MIT/BIH Arrhythmia Database. IEEE Trans. Biomed. Eng. 33 (12), 1157-1165.
  9. Köhler, B., Hennig, C., Orglmeister, R., 2002. The principles of software QRS detection. IEEE Eng. Med. Biol. Mag. 21 (1), 42-57.
  10. MIT-BIH, 1998. Database Distribution. Massachusetts Institute of Technology, Cambridge, MA. Available: http://ecg.mit.edu/.
  11. Pan, J., Tompkins, W., 1985. A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32 (3), 230-236.
  12. Rangayyan, R., 2002. Biomedical signal analysis: a casestudy approach. Wiley: New York.
  13. Rezk, S., Join, C., Asmi, S., 2011. An algebraic derivative-based method for R wave detection. Proceedings of the 19th European Signal Processing Conference, EUSIPCO 2011, Barcelona, Spain, August 29 - September 2, 2011. 1578-1582.
  14. Ritter, P., Becker, R., Freyer, F., Villringer, A., 2010. EEG quality: the image acquisition artifact. In C. Mulert, L. Limieux (eds.), EEG-fMRI: Physiological basis, technique and applications. Springer: Verlag, Berlin, Heidelberg.
  15. Thakor, N., Webster, J., Tompkins, W., 1984. Estimation of QRS complex power spectra for design a QRS filter. IEEE Trans. Biomed. Eng. 31 (11), 702-706.
  16. Van de Velde, R., Van Erp, G., Cluitmans, P., 1998. Detection of muscle artefact in the normal human awake EEG. Electroencephalogr. Clin. Neurophysiol. 107, 149-158.
Download


Paper Citation


in Harvard Style

Ferreira J., J. M. Cluitmans P. and M. Aarts R. (2013). Detection of Sharp Wave Activity in Biological Signals using Differentiation between Consecutive Samples . 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 327-332. DOI: 10.5220/0004245003270332


in Bibtex Style

@conference{biosignals13,
author={José L. Ferreira and Pierre J. M. Cluitmans and Ronald M. Aarts},
title={Detection of Sharp Wave Activity in Biological Signals using Differentiation between Consecutive Samples},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)},
year={2013},
pages={327-332},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004245003270332},
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 - Detection of Sharp Wave Activity in Biological Signals using Differentiation between Consecutive Samples
SN - 978-989-8565-36-5
AU - Ferreira J.
AU - J. M. Cluitmans P.
AU - M. Aarts R.
PY - 2013
SP - 327
EP - 332
DO - 10.5220/0004245003270332