Skip to main content

Waveform Integrity in Atrial Fibrillation: The Forgotten Issue of Cardiac Electrophysiology

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.


Atrial fibrillation (AF) is the most common arrhythmia in clinical practice with an increasing prevalence of about 15% in the elderly. Despite other alternatives, catheter ablation is currently considered as the first-line therapy for the treatment of AF. This strategy relies on cardiac electrophysiology systems, which use intracardiac electrograms (EGM) as the basis to determine the cardiac structures contributing to sustain the arrhythmia. However, the noise-free acquisition of these recordings is impossible and they are often contaminated by different perturbations. Although suppression of nuisance signals without affecting the original EGM pattern is essential for any other later analysis, not much attention has been paid to this issue, being frequently considered as trivial. The present work introduces the first thorough study on the significant fallout that regular filtering, aimed at reducing acquisition noise, provokes on EGM pattern morphology. This approach has been compared with more refined denoising strategies. Performance has been assessed both in time and frequency by well established parameters for EGM characterization. The study comprised synthesized and real EGMs with unipolar and bipolar recordings. Results reported that regular filtering altered substantially atrial waveform morphology and was unable to remove moderate amounts of noise, thus turning time and spectral characterization of the EGM notably inaccurate. Methods based on Wavelet transform provided the highest ability to preserve EGM morphology with improvements between 20 and beyond 40%, to minimize dominant atrial frequency estimation error with up to 25% reduction, as well as to reduce huge levels of noise with up to 10 dB better reduction. Consequently, these algorithms are recommended as a replacement of regular filtering to avoid significant alterations in the EGMs. This could lead to more accurate and truthful analyses of atrial activity dynamics aimed at understanding and locating the sources of AF.

This is a preview of subscription content, access via your institution.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8


  1. 1.

    Addison, P. S. Wavelet transforms and the ECG: a review. Physiol. Meas. 26(5):R155–R199, 2005.

    Article  PubMed  Google Scholar 

  2. 2.

    Aksu, T., T. E. Guler, K. Yalin, and A. Oto. Unanswered questions in complex fractionated atrial electrogram ablation. Pacing Clin. Electrophysiol. 39(11):1269–1278, 2016.

    Article  PubMed  Google Scholar 

  3. 3.

    Alcaraz, R., F. Hornero, and J. J. Rieta. Assessment of non-invasive time and frequency atrial fibrillation organization markers with unipolar atrial electrograms. Physiol. Meas. 32(1):99–114, 2011.

    Article  PubMed  Google Scholar 

  4. 4.

    Atienza, F., J. Almendral, J. Jalife, S. Zlochiver, R. Ploutz-Snyder, E. G. Torrecilla, A. Arenal, J. Kalifa, F. Fernández-Avilés, and O. Berenfeld. Real-time dominant frequency mapping and ablation of dominant frequency sites in atrial fibrillation with left-to-right frequency gradients predicts long-term maintenance of sinus rhythm. Heart Rhythm 6(1):33–40, 2009.

    Article  PubMed  Google Scholar 

  5. 5.

    Atienza, F., J. Almendral, J. Moreno, R. Vaidyanathan, A. Talkachou, J. Kalifa, A. Arenal, J. P. Villacastín, E. G. Torrecilla, A. Sánchez, R. Ploutz-Snyder, J. Jalife, and O. Berenfeld. Activation of inward rectifier potassium channels accelerates atrial fibrillation in humans: evidence for a reentrant mechanism. Circulation 114(23):2434–2442, 2006.

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Blanco-Velasco, M., B. Weng, and K. E. Barner. ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Comput. Biol. Med. 38(1):1–13, 2008.

    Article  PubMed  Google Scholar 

  7. 7.

    Boardman, A., F. S. Schlindwein, A. P. Rocha, and A. Leite. A study on the optimum order of autoregressive models for heart rate variability. Physiol. Meas. 23(2):325–336, 2002.

    Article  PubMed  Google Scholar 

  8. 8.

    Botteron, G. W. and J. M. Smith. A technique for measurement of the extent of spatial organization of atrial activation during atrial fibrillation in the intact human heart. IEEE Trans. Biomed. Eng. 42(6):579–586, 1995.

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Castells, F., R. Cervigón, and J. Millet. On the preprocessing of atrial electrograms in atrial fibrillation: understanding Botteron’s approach. Pacing Clin. Electrophysiol. 37(2):133–143, 2014.

    Article  PubMed  Google Scholar 

  10. 10.

    Chang, K.-M. Ensemble empirical mode decomposition for high frequency ECG noise reduction. Biomed. Tech. (Berl.) 55(4):193–201, 2010.

    Article  Google Scholar 

  11. 11.

    Chen, S.-W. and Y.-H. Chen. Hardware design and implementation of a wavelet de-noising procedure for medical signal preprocessing. Sensors (Basel) 15(10):26396–26414, 2015.

    Article  Google Scholar 

  12. 12.

    Chugh, S. S., R. Havmoeller, K. Narayanan, D. Singh, M. Rienstra, E. J. Benjamin, R. F. Gillum, Y.-H. Kim, J. H. McAnulty, Jr, Z.-J. Zheng, M. H. Forouzanfar, M. Naghavi, G. A. Mensah, M. Ezzati, and C. J. L. Murray. Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 Study. Circulation 129(8):837–847, 2014.

    Article  PubMed  Google Scholar 

  13. 13.

    Ciaccio, E. J., A. B. Biviano, and H. Garan. Computational method for high resolution spectral analysis of fractionated atrial electrograms. Comput. Biol. Med. 43(10):1573–1582, 2013.

    Article  PubMed  Google Scholar 

  14. 14.

    Corino, V. D. A., M. W. Rivolta, R. Sassi, F. Lombardi, and L. T. Mainardi. Ventricular activity cancellation in electrograms during atrial fibrillation with constraints on residuals’ power. Med. Eng. Phys. 35(12):1770–1777, 2013.

    Article  PubMed  Google Scholar 

  15. 15.

    de Bakker, J. M. T. and F. H. M. Wittkampf. The pathophysiologic basis of fractionated and complex electrograms and the impact of recording techniques on their detection and interpretation. Circ. Arrhythm. Electrophysiol. 3(2):204–213, 2010.

    Article  PubMed  Google Scholar 

  16. 16.

    Donoho, D. and I. Johnstone. Ideal spatial adaptation by wavelet shrinkage. Biometrika 81:425–455, 1994.

    Article  Google Scholar 

  17. 17.

    Donoho, D. and I. Johnstone. Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc. 90:1200–1224, 1995.

    Article  Google Scholar 

  18. 18.

    Everett, IV, T. H., L. C. Kok, R. H. Vaughn, J. R. Moorman, and D. E. Haines. Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy. IEEE Trans. Biomed. Eng. 48(9):969–978, 2001.

    Article  PubMed  Google Scholar 

  19. 19.

    Faes, L., G. Nollo, R. Antolini, F. Gaita, and F. Ravelli. A method for quantifying atrial fibrillation organization based on wave-morphology similarity. IEEE Trans. Biomed. Eng. 49(12 Pt 2):1504–1513, 2002.

    Article  PubMed  Google Scholar 

  20. 20.

    Flandrin, P., G. Rilling, and P. Goncalves. Empirical mode decomposition as a filter bank. IEE Signal Process. Lett. 11:112–114, 2004.

    Article  Google Scholar 

  21. 21.

    Gutiérrez-Gnecchi, J. A., R. Morfin-Magana, D. Lorias-Espinoza, A. C. Tellez-Anguiano, E. Reyes-Archundia, A. Méndez-Patino, and R. Castaneda-Miranda. DSP-based arrhythmia classification using wavelet transform and probabilistic neural network. Biomed. Signal Process. Control 32:44–56, 2017.

    Article  Google Scholar 

  22. 22.

    Haïssaguerre, M., M. Hocini, A. Denis, A. J. Shah, Y. Komatsu, S. Yamashita, M. Daly, S. Amraoui, S. Zellerhoff, M.-Q. Picat, A. Quotb, L. Jesel, H. Lim, S. Ploux, P. Bordachar, G. Attuel, V. Meillet, P. Ritter, N. Derval, F. Sacher, O. Bernus, H. Cochet, P. Jaïs, and R. Dubois. Driver domains in persistent atrial fibrillation. Circulation 130(7):530–538, 2014.

    Article  PubMed  Google Scholar 

  23. 23.

    Heijman, J., V. Algalarrondo, N. Voigt, J. Melka, X. H. T. Wehrens, D. Dobrev, and S. Nattel. The value of basic research insights into atrial fibrillation mechanisms as a guide to therapeutic innovation: a critical analysis. Cardiovasc. Res. 109(4):467–479, 2016.

    CAS  Article  PubMed  Google Scholar 

  24. 24.

    Houben, R. P. M. and M. A. Allessie. Processing of intracardiac electrograms in atrial fibrillation. Diagnosis of electropathological substrate of AF. IEEE Eng. Med. Biol. Mag. 25(6):40–51, 2006.

    Article  PubMed  Google Scholar 

  25. 25.

    Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A 454:903–995, 1998.

    Article  Google Scholar 

  26. 26.

    Issa, Z. F., J. W. Miller, and D. P. Zipes. Clinical Arrhythmology and Electrophysiology: A Comparison to Braunwald’s Heart Disease, 2nd ed. Amsterdam: Elsevier, 2012.

    Google Scholar 

  27. 27.

    Jenkal, W., R. Latif, A. Toumanari, et al. An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform. Biocybern. Biomed. Eng. 36(3):499–508, 2016.

    Article  Google Scholar 

  28. 28.

    Kabir, M. A. and C. Shahnaz. Denoising ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomed. Signal Process. Control 7:481–489, 2012.

    Article  Google Scholar 

  29. 29.

    Koutalas, E., S. Rolf, B. Dinov, S. Richter, A. Arya, A. Bollmann, G. Hindricks, and P. Sommer. Contemporary mapping techniques of complex cardiac arrhythmias–identifying and modifying the arrhythmogenic substrate. Arrhythm. Electrophysiol. Rev. 4(1):19–27, 2015.

    Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Lahmiri, S. Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains. Healthc. Technol. Lett. 1(3):104–109, 2014.

    Article  Google Scholar 

  31. 31.

    Lian, J., G. Garner, D. Muessing, and V. Lang. A simple method to quantify the morphological similarity between signals. Signal Process. 90:684–688, 2010.

    Article  Google Scholar 

  32. 32.

    Liang, H., Q.-H. Lin, and J. D. Z. Chen. Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease. IEEE Trans. Biomed. Eng. 52(10):1692–1701, 2005.

    Article  PubMed  Google Scholar 

  33. 33.

    Luo, S. and P. Johnston. A review of electrocardiogram filtering. J. Electrocardiol. 43(6):486–496, 2010.

    Article  PubMed  Google Scholar 

  34. 34.

    Mallat, S. A Wavelet Tour of Signal Processing. Burlington: Academic Press, 1999.

    Google Scholar 

  35. 35.

    Narayan, S. M. and J. A. B. Zaman. Mechanistically based mapping of human cardiac fibrillation. J. Physiol. 594(9):2399–2415, 2016.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Nedios, S., P. Sommer, A. Bollmann, and G. Hindricks. Advanced mapping systems to guide atrial fibrillation ablation: electrical information that matters. J. Atr. Fibrillation 8(6):1337, 2016.

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Ng, J., A. I. Borodyanskiy, E. T. Chang, R. Villuendas, S. Dibs, A. H. Kadish, and J. J. Goldberger. Measuring the complexity of atrial fibrillation electrograms. J. Cardiovasc. Electrophysiol. 21(6):649–655, 2010.

    Article  PubMed  Google Scholar 

  38. 38.

    Ng, J. and J. J. Goldberger, eds. Intracardiac electrograms. In Practical Signal and Image Processing in Clinical Cardiology. London: Springer, 2010, pp. 319–348.

  39. 39.

    Ng, J., A. H. Kadish, and J. J. Goldberger. Technical considerations for dominant frequency analysis. J. Cardiovasc. Electrophysiol. 18(7):757–764, 2007.

    Article  PubMed  Google Scholar 

  40. 40.

    Ng, J., V. Sehgal, J. K. Ng, D. Gordon, and J. J. Goldberger. Iterative method to detect atrial activations and measure cycle length from electrograms during atrial fibrillation. IEEE Trans. Biomed. Eng. 61(2):273–278, 2014.

    Article  PubMed  Google Scholar 

  41. 41.

    Nollo, G., M. Marconcini, L. Faes, F. Bovolo, F. Ravelli, and L. Bruzzone. An automatic system for the analysis and classification of human atrial fibrillation patterns from intracardiac electrograms. IEEE Trans. Biomed. Eng. 55(9):2275–2285, 2008.

    Article  PubMed  Google Scholar 

  42. 42.

    Oesterlein, T. G., G. Lenis, D.-T. Rudolph, A. Luik, B. Verma, C. Schmitt, and O. Dössel. Removing ventricular far-field signals in intracardiac electrograms during stable atrial tachycardia using the periodic component analysis. J. Electrocardiol. 48(2):171–180, 2015.

    Article  PubMed  Google Scholar 

  43. 43.

    Poornachandra, S. and N. Kumaravel. A novel method for the elimination of power line frequency in ECG signal using hyper shrinkage function. Digital Signal Process. 18(2):116–126, 2008.

    Article  Google Scholar 

  44. 44.

    Potter, B. J. and J. Le Lorier. Taking the pulse of atrial fibrillation. Lancet 386(9989):113–115, 2015.

    Article  PubMed  Google Scholar 

  45. 45.

    Rafiee, J., M. A. Rafiee, N. Prause, and M. P. Schoen. Wavelet basis functions in biomedical signal processing. Expert Syst. Biomed. Signal Process. 38:6190–6201, 2011.

    Google Scholar 

  46. 46.

    Ravelli, F., M. Masè, A. Cristoforetti, M. Marini, and M. Disertori. The logical operator map identifies novel candidate markers for critical sites in patients with atrial fibrillation. Prog. Biophys. Mol. Biol. 115(2–3):186–197, 2014.

    Article  PubMed  Google Scholar 

  47. 47.

    Sanchez, C., J. J. Rieta, F. Castells, J. Ródenas, and J. Millet. Atrial activity extraction in Holter registers using adaptive Wavelet analysis. Annual International Conference of Computers in Cardiology, vol. 30, pp. 569–572, 2003.

  48. 48.

    Sanders, P., O. Berenfeld, M. Hocini, P. Jaïs, R. Vaidyanathan, L.-F. Hsu, S. Garrigue, Y. Takahashi, M. Rotter, F. Sacher, C. Scavée, R. Ploutz-Snyder, J. Jalife, and M. Haïssaguerre. Spectral analysis identifies sites of high-frequency activity maintaining atrial fibrillation in humans. Circulation 112(6):789–797, 2005.

    Article  PubMed  Google Scholar 

  49. 49.

    Schnabel, R. B., X. Yin, P. Gona, M. G. Larson, A. S. Beiser, D. D. McManus, C. Newton-Cheh, S. A. Lubitz, J. W. Magnani, P. T. Ellinor, S. Seshadri, P. A. Wolf, R. S. Vasan, E. J. Benjamin, and D. Levy. 50 year trends in atrial fibrillation prevalence, incidence, risk factors, and mortality in the Framingham Heart Study: a cohort study. Lancet 386(9989):154–162, 2015.

    Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Schotten, U., D. Dobrev, P. G. Platonov, H. Kottkamp, and G. Hindricks. Current controversies in determining the main mechanisms of atrial fibrillation. J. Intern. Med. 279(5):428–438, 2016.

    CAS  Article  PubMed  Google Scholar 

  51. 51.

    Singh, B. N. and A. K. Tiwari. Optimal selection of wavelet basis function applied to ECG signal denoising. Digital Signal Process. 16:275–287, 2006.

    Article  Google Scholar 

  52. 52.

    Smital, L., M. Vítek, J. Kozumplík, and I. Provazník. Adaptive wavelet Wiener filtering of ECG signals. IEEE Trans. Biomed. Eng. 60(2):437–445, 2013.

    Article  PubMed  Google Scholar 

  53. 53.

    Stevenson, W. G. and K. Soejima. Recording techniques for clinical electrophysiology. J. Cardiovasc. Electrophysiol. 16(9):1017–1022, 2005.

    Article  PubMed  Google Scholar 

  54. 54.

    Tikkanen, P. E. Nonlinear wavelet and wavelet packet denoising of electrocardiogram signal. Biol. Cybern. 80(4):259–267, 1999.

    CAS  Article  PubMed  Google Scholar 

  55. 55.

    Venkatachalam, K. L., J. E. Herbrandson, and S. J. Asirvatham. Signals and signal processing for the electrophysiologist. Part I: electrogram acquisition. Circ. Arrhythm. Electrophysiol. 4(6):965–973, 2011.

  56. 56.

    Venkatachalam, K. L., J. E. Herbrandson, and S. J. Asirvatham. Signals and signal processing for the electrophysiologist. Part II: signal processing and artifact. Circ. Arrhythm. Electrophysiol. 4(6):974–981, 2011.

  57. 57.

    Wodchis, W. P., R. S. Bhatia, K. Leblanc, N. Meshkat, and D. Morra. A review of the cost of atrial fibrillation. Value Health 15(2):240–248, 2012.

    Article  PubMed  Google Scholar 

  58. 58.

    Wynn, G. J., M. Das, L. J. Bonnett, S. Panikker, T. Wong, and D. Gupta. Efficacy of catheter ablation for persistent atrial fibrillation: a systematic review and meta-analysis of evidence from randomized and nonrandomized controlled trials. Circ. Arrhythm. Electrophysiol. 7(5):841–852, 2014.

    Article  PubMed  Google Scholar 

  59. 59.

    Xiong, F., X. Qi, S. Nattel, and P. Comtois. Wavelet analysis of cardiac optical mapping data. Comput. Biol. Med. 65:243–255, 2015.

    Article  PubMed  Google Scholar 

  60. 60.

    Zoni-Berisso, M., F. Lercari, T. Carazza, and S. Domenicucci. Epidemiology of atrial fibrillation: European perspective. Clin. Epidemiol. 6:213–220, 2014.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


This work was supported by the projects TEC2014-52250-R from the Spanish Ministry of Economy and Competitiveness and PPII-2014-026-P from Junta de Comunidades de Castilla–La Mancha.

Conflict of Interest

The authors declare no conflict of interest.

Author information



Corresponding author

Correspondence to Miguel Martínez-Iniesta.

Additional information

Associate Editor Ender A Finol oversaw the review of this article.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 211 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Martínez-Iniesta, M., Ródenas, J., Alcaraz, R. et al. Waveform Integrity in Atrial Fibrillation: The Forgotten Issue of Cardiac Electrophysiology. Ann Biomed Eng 45, 1890–1907 (2017).

Download citation


  • Atrial fibrillation
  • Electrogram
  • Filtering
  • Wavelet transform
  • Empirical mode decomposition