ECG P-WAVE SMOOTHING AND DENOISING BY QUADRATIC VARIATION REDUCTION

Antonio Fasano, Valeria Villani, Luca Vollero, Federica Censi

Abstract

Atrial fibrillation is the most common persistent cardiac arrhythmia and it is characterized by a disorganized atrial electrical activity. Its occurrence can be detected, and even predicted, through P-waves time-domain and morphological analysis in ECG tracings. Given the low signal-to-noise ratio associated to P-waves, such analysis are possible if noise and artifacts are effectively filtered out from P-waves. In this paper a novel smoothing and denoising algorithm for P-waves is proposed. The algorithm is solution to a convex optimization problem. Smoothing and denoising are achieved reducing the quadratic variation of the measured P-waves. Simulation results confirm the effectiveness of the approach and show that the proposed algorithm is remarkably good at smoothing and denoising P-waves. The achieved SNR gain exceeds 15 dB for input SNR below 6 dB. Moreover the proposed algorithm has a computational complexity that is linear in the size of the vector to be processed. This property makes it suitable also for real-time applications.

References

  1. Bayes de Luna, A., Guindo, J., Vinolas, X., MartinezRubio, A., Oter, R., and Bayes-Genis, A. (1999). Third-degree inter-atrial block and supraventricular tachyarrhythmias. Europace, 1:43-46.
  2. Benjamin, E. J., Wolf, P. A., D'Agostino, R. B., Silbershatz, H., Kannel, W. B., and Levy, D. (1998). Impact of atrial fibrillation on the risk of death: the Framingham Heart Study. Circulation, 98:946-952.
  3. Boyd, S. and Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press.
  4. Carlson, J., Johansson, R., and Olsson, S. B. (2001). Classification of electrocardiographic P-wave morphology. IEEE Trans Biomed Eng, 48:401-405.
  5. Censi, F., Calcagnini, G., Ricci, C., Ricci, R. P., Santini, M., Grammatico, A., and Bartolini, P. (2007). Pwave morphology assessment by a gaussian functionsbased model in atrial fibrillation patients. IEEE Trans Biomed Eng, 54:663-672.
  6. Censi, F., Ricci, C., Calcagnini, G., Triventi, M., Ricci, R. P., Santini, M., and Bartolini, P. (2008). Timedomain and morphological analysis of the P-wave. Part I: Technical aspects for automatic quantification of P-wave features. Pacing Clin Electrophysiol, 31:874-883.
  7. Clifford, G. D., Azuaje, F., and McSharry, P. (2006). Advanced Methods And Tools for ECG Data Analysis. Artech House, Inc., Norwood, MA, USA.
  8. Dilaveris, P. E. and Gialafos, J. E. (2002). Future concepts in P wave morphological analyses. Card Electrophysiol Rev, 6:221-224.
  9. Feinberg, W. M., Blackshear, J. L., Laupacis, A., Kronmal, R., and Hart, R. G. (1995). Prevalence, age distribution, and gender of patients with atrial fibrillation. Analysis and implications. Arch. Intern. Med., 155:469-473.
  10. Go, A. S., Hylek, E. M., Phillips, K. A., Chang, Y., Henault, L. E., Selby, J. V., and Singer, D. E. (2001). Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. JAMA, 285:2370- 2375.
  11. Golub, G. H. and Van Loan, C. F. (1996). Matrix computations. Johns Hopkins University Press, Baltimore, MD, USA, 3 edition.
  12. Horn, R. A. and Johnson, C. R. (1990). Matrix Analysis. Cambridge University Press.
  13. McBride, D., Mattenklotz, A. M., Willich, S. N., and Bruggenjurgen, B. (2008). The Costs of Care in Atrial Fibrillation and the Effect of Treatment Modalities in Germany. Value Health.
  14. Nattel, S., Burstein, B., and Dobrev, D. (2008). Atrial remodeling and atrial fibrillation: mechanisms and implications. Circ Arrhythm Electrophysiol, 1(1):62-73.
  15. Oppenheim, A. V., Schafer, R. W., and Buck, J. R. (1999). Discrete-time signal processing (2nd ed.). PrenticeHall, Inc.
  16. Perez, M. V., Dewey, F. E., Marcus, R., Ashley, E. A., Al-Ahmad, A. A., Wang, P. J., and Froelicher, V. F. (2009). Electrocardiographic predictors of atrial fibrillation. Am. Heart J., 158:622-628.
  17. Platonov, P. G., Carlson, J., Ingemansson, M. P., Roijer, A., Hansson, A., Chireikin, L. V., and Olsson, S. B. (2000). Detection of inter-atrial conduction defects with unfiltered signal-averaged P-wave ECG in patients with lone atrial fibrillation. Europace, 2:32-41.
  18. Shreve, S. E. (2004). Stochastic Calculus for Finance II: Continuous-Time Models. Springer Finance. Springer Science+Business Media, Inc.
Download


Paper Citation


in Harvard Style

Fasano A., Villani V., Vollero L. and Censi F. (2011). ECG P-WAVE SMOOTHING AND DENOISING BY QUADRATIC VARIATION REDUCTION . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011) ISBN 978-989-8425-35-5, pages 289-294. DOI: 10.5220/0003169202890294


in Bibtex Style

@conference{biosignals11,
author={Antonio Fasano and Valeria Villani and Luca Vollero and Federica Censi},
title={ECG P-WAVE SMOOTHING AND DENOISING BY QUADRATIC VARIATION REDUCTION},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)},
year={2011},
pages={289-294},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003169202890294},
isbn={978-989-8425-35-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)
TI - ECG P-WAVE SMOOTHING AND DENOISING BY QUADRATIC VARIATION REDUCTION
SN - 978-989-8425-35-5
AU - Fasano A.
AU - Villani V.
AU - Vollero L.
AU - Censi F.
PY - 2011
SP - 289
EP - 294
DO - 10.5220/0003169202890294