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Current Medical and Technical Concepts in the Analysis of Endocardial Signals in Atrial Fibrillation

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Biomedical Engineering Aims and scope

Atrial fibrillation (AF) is the commonest arrhythmia seen in clinical practice, though our understanding of the mechanisms of its generation, propagation, and reinitiation remains incomplete. This is limiting not only from the scientific point of view, but also from the practical, as regulatory documentation for the treatment of this pathology cannot be developed without an accepted theory. There has been a recent increase in interest in a theory based on the observation that spiral waves, or rotors, with specific properties for each atrium, are the source of the trigger for fibrillation and may therefore serve as targets for radio-frequency treatment in low-invasive endocardial procedures. There is also an approach based in seeking areas of the atrium in which complex fractionated atrial endograms (CFAE) can be recorded. We present here the basic concepts of analysis of atrial signals during atrial fibrillation, reflecting both the technical and medical aspects.

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References

  1. Bollmann, A., Husser, D., Mainardi, L., Lombardi, F., Langley, P., Murray, A., Rieta, J. J., Millet, J., Olsson, S. B., Stridh, M., and Sörnmo, L., “Analysis of surface electrocardiograms in atrial fibrillation: Techniques, research, and clinical applications,” Europace, 8, No. 11, 911-926 (2006).

    Article  Google Scholar 

  2. Sörnmo, L., Stridh, M., Husser, D., Bollmann, A., and Olsson, S. B., “Analysis of atrial fibrillation: From electrocardiogram signal processing to clinical management,” Philos. Trans. A Math. Phys. Eng. Sci., 367, No. 1887, 235-253 (2009).

    Article  MathSciNet  MATH  Google Scholar 

  3. Slocum, J., Byrom, E., McCarthy, L., Sahakian, A., and Swiryn, S., “Computer detection of atrioventricular dissociation from surface electrocardiograms during wide QRS complex tachycardias,” Circulation, 72, No. 5, 1028-1036 (1985).

    Article  Google Scholar 

  4. Rieta, J. J., Castells, F., Sánchez, C., Zarzoso, V., and Millet, J., “Atrial activity extraction for atrial fibrillation analysis using blind source separation,” IEEE Trans. Biomed. Eng., 51, No. 7, 1176-1186 (2004).

    Article  Google Scholar 

  5. Holm, M., Pehrson, S., Ingemansson, M., Sörnmo, L., Johansson, R., Sandhall, L., Sunemark, M., Smideberg, B., Olsson, C., and Olsson, S. B., “Non-invasive assessment of the atrial cycle length during atrial fibrillation in man: Introducing, validating and illustrating a new ECG method,” Cardiovasc. Res., 38, No. 1, 69-81 (1998).

    Article  Google Scholar 

  6. Niwano, S., Sasaki, T., Kurokawa, S., Kiryu, M., Fukaya, H., Hatakeyama, Y., Niwano, H., Fujiki, A., and Izumi, T., “Predicting the efficacy of antiarrhythmic agents for interrupting persistent atrial fibrillation according to spectral analysis of the fibrillation waves on the surface ECG,” Circulation, 73, No. 7, 1210-1218 (2009).

    Article  Google Scholar 

  7. Bollmann, A., Kanuru, N. K., McTeague, K. K., Walter, P. F., DeLurgio, D. B., and Langberg, J. J., “Frequency analysis of human atrial fibrillation using the surface electrocardiogram and its response to ibutilide,” Am. J. Cardiol., 81, No. 12, 1439-1445 (1998).

    Article  Google Scholar 

  8. Langberg, J., Burnette, J. C., and McTeague, K. K., “Spectral analysis of the electrocardiogram predicts recurrence of atrial fibrillation after cardioversion,” J. Electrocardiol., 31, 80-84 (1998).

    Article  Google Scholar 

  9. Faes, L., Nollo, G., Antolini, R., Gaita, F., and Ravelli, F., “A method for quantifying atrial fibrillation organization based on wave-morphology similarity,” IEEE Trans. Biomed. Eng., 49, No. 12, 1504-1513 (2002).

    Article  Google Scholar 

  10. Alcaraz, R. and Rieta, J. J., “A review on sample entropy applications for the non-invasive analysis of atrial fibrillation electrocardiograms,” Biomed. Signal Process Control, 5, 1-14 (2010).

    Article  Google Scholar 

  11. Sih, H. J., “Measures of organization during atrial fibrillation,” Annali dell’Istituto Superior di Sanitа, 37, No. 3, 361-369 (2001).

  12. Nademanee, K., McKenzie, J., Kosar, E., Schwab, M., Sunsaneewitayakul, B., Vasavakul, T., Khunnawat, C., and Ngarmukos, T., “A new approach for catheter ablation of atrial fibrillation: Mapping of the electrophysiologic substrate,” J. Am. Coll. Cardiol., 43, No. 11, 2054-2056 (2004).

    Article  Google Scholar 

  13. Shan, Z., Van Der Voort, P. H., Blaauw, Y., Duytschaever, M., and Allessie, M. A., “Fractionation of electrograms and linking of activation during pharmacologic cardioversion of persistent atrial fibrillation in the goat,” J. Cardiovasc. Electrophysiol., 15, No. 5, 572-580 (2004).

    Article  Google Scholar 

  14. Dosdall, D. J. and Ideker, R. E., “Intracardiac atrial defibrillation,” Heart Rhythm, 4, No. 3, 51-56 (2007).

    Article  Google Scholar 

  15. Shkurovich, S., Sahakian, A. V., and Swiryn, S., “Detection of atrial activity from high-voltage leads of implantable ventricular defibrillators using a cancellation technique,” IEEE Trans. Biomed. Eng., 45, No. 2, 229-234 (1998).

    Article  Google Scholar 

  16. Mase, M., Faes, L., Antolini, R., Scaglione, M., and Ravelli, F., “Quantification of synchronization during atrial fibrillation by Shannon entropy: Validation in patients and computer model of atrial arrhythmias,” Physiol. Meas., 26, No. 6, 911-923 (2005).

    Article  Google Scholar 

  17. Houben, R. P. and Allessie, M. A., “Processing of intracardiac electrograms in atrial fibrillation. Diagnosis of electropathological substrate of AF,” IEEE Eng. Med. Biol. Mag., 25, No. 6, 40-51 (2006).

    Article  Google Scholar 

  18. Rieta, J. J. and Hornero, F., “Comparative study of methods for ventricular activity cancellation in atrial electrograms of atrial fibrillation,” Physiol. Meas., 28, No. 8, 925-936 (2007).

    Article  Google Scholar 

  19. Widrow, B., Glover, J. R., McCool, J. M., et al., “Adaptive noise cancelling: Principles and applications,” Proc. IEEE, 63, No. 12, 1692-1716 (1975).

    Article  Google Scholar 

  20. Malmivuo, J. and Plonsey, R. Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields, Oxford University Press (1995), p. 358.

  21. Hyvarinen, A., Karhunen, J., and Oja, E., Independent Component Analysis, John Wiley & Sons, Inc. (2001), p. 412.

  22. Welch, P. D., “Use of Fast Fourier Transform for estimation of power spectra: A method based on time averaging over short modified periodograms,” IEEE Trans. Audio Electroacoust., 15, No. 2, 70-73 (1967).

    Article  Google Scholar 

  23. Hamming, R. W., Digital Filters, Prentice-Hall signal processing series, Prentice-Hall, Englewood Cliffs (1977), p. 283.

  24. Manolakis, D. G., Ingle, V. K., and Kogon, S. M., Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering, and Array Processing, Artech House, Boston (2005), p. 485.

    Google Scholar 

  25. Najim, M., Modeling, Estimation and Optimal Filtering in Signal Processing, Digital Signal and Image Processing Series, J. Wiley & Sons, London (2008), p. 372.

    Book  Google Scholar 

  26. Stridh, M., Sörnmo, L., Meurling, C. J., and Olsson, S. B., “Characterization of atrial fibrillation using the surface ECG: Time-dependent spectral properties,” IEEE Trans. Biomed. Eng., 48, No. 1, 19-27 (2001).

    Article  Google Scholar 

  27. Shkurovich, S., Sahakian, A. V., and Swiryn, S., “Detection of atrial activity from high-voltage leads of implantable ventricular defibrillators using a cancellation technique,” IEEE Trans. Biomed. Eng., 45, No. 2, 229-234 (1998).

    Article  Google Scholar 

  28. Kuleshov, A. P., Ilyin, A. V., and Zaretsky, A. P., “Continuous visualization of P–Q intervals in portable devices for monitoring human organism functional state,” Sovremen. Tekhnol. Med., 8, No. 1, 41-47 (2016).

    Article  Google Scholar 

  29. Cohen, L., Time-Frequency Analysis, Prentice Hall PTR, Englewood Cliffs, N. J. (1995), p. 451.

  30. Boashash, B., “Estimating and interpreting the instantaneous frequency of a signal. Algorithms and applications,” Proc. IEEE, 80, No. 4, 540-568 (1992).

    Article  Google Scholar 

  31. Everett, T. H., 4th, Moorman, J. R., Kok, L. C., Akar, J. G., and Haines, D. E., “Assessment of global atrial fibrillation organization to optimize timing of atrial defibrillation,” Circulation, 103, No. 23, 2857-2861 (2001).

  32. Stridh, M., Sörnmo, L., Meurling, C. J., and Olsson, S. B., “Sequential characterization of atrial tachyarrhythmias based on ECG time−frequency analysis,” IEEE Trans. Biomed. Eng., 51, No. 1, 100-114 (2004).

    Article  Google Scholar 

  33. Sandberg, F., Stridh, M., and Sörnmo, L., “Frequency tracking of atrial fibrillation using hidden Markov models,” IEEE Trans. Biomed. Eng., 55, No. 2, 502-511 (2008).

    Article  Google Scholar 

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Correspondence to A. P. Zaretskiy.

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Translated from Meditsinskaya Tekhnika, Vol. 51, No. 3, May-Jun., 2017, pp. 23-27.

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Zaretskiy, A.P., Kuleshov, A.P. & Gromyko, G.A. Current Medical and Technical Concepts in the Analysis of Endocardial Signals in Atrial Fibrillation. Biomed Eng 51, 183–188 (2017). https://doi.org/10.1007/s10527-017-9711-x

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  • DOI: https://doi.org/10.1007/s10527-017-9711-x

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