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Determining Dominant Frequency with Data-Adaptive Windows

  • Gagan Mirchandani
  • Shruti Sharma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)

Abstract

Measurement of activation rates in cardiac electrograms is commonly done though estimating the frequency of the sinusoid with the greatest power. This frequency, commonly referred to as Dominant Frequency, is generally estimated using the short-time Fourier Transform with a window of fixed size. In this work a new short-time Fourier transform method with a data-adaptive window is introduced. Experiments are conducted with both synthetic and real data. Results for the former case are compared with current state-of-the-art methods. Given the difficulty in identifying activation points in electrograms, experiments reported in the literature have so far used only synthetic data. The new method is tested by application to real data, with true activation rates determined manually. Substantial improvement is observed. An error analysis is provided.

Keywords

atrial fibrillation data-adaptive windows non-stationary signals dominant frequency 

References

  1. 1.
    Addison, P.S., Watson, J.N., Clegg, G.R., Steen, P., Robertson, C.E.: Finding coordinated atrial activity during ventricular fibrillation using wavelet decomposition. IEEE Engineering in Medicine and Biology 58–65 (January/February 2002)Google Scholar
  2. 2.
    Barbaro, V., Bartolini, P., Calcagnini, G., Censi, F., Michelucci, A., Morelli, S.: Mapping the Organization of human atrial fibrillation using a basket catheter. Computers in Cardiology, 475–478 (1999)Google Scholar
  3. 3.
    Ellis, W.S., Eisenberg, S.J., Auslander, D.M., DAe, M.W., Zakhor, A., Lesh, M.D.: Deconvolution: A novel signal processing approach for determining activation time from fractionated electrograms and detecting infarcted tissue. Circulation 94, 2633–2640 (1996)Google Scholar
  4. 4.
    Elvan, A., et al.: Dominant Frequency of Atrial Fibrillation Correlates Poorly with Atrial Fibrillation Cycle Length. Circulation, Arrhythmia and Electrophysiology 2, 634–644 (2009)CrossRefGoogle Scholar
  5. 5.
    Everett, T.H., Kok, L.-C., Vaughn, R.H., Moorman, J.R., Haines, D.E.: Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy. IEEE Transactions on Biomedical Engineering 48(9), 69–978 (2001)CrossRefGoogle Scholar
  6. 6.
    Fischer, G., Stühlinger, M.C., Wieser, B., Nowak, C.-N., Wieser, L., Tilg, B., Hintringer, F.: On Computing Dominant Frequency From Bipolar Intracardiac Electrograms. IEEE Transactions on Biomedical Engineering 54(1), 165–169 (2007)CrossRefGoogle Scholar
  7. 7.
    Le Goazigo, C.: Measurement of the dominant frequency in atrial fibrillation electrograms. MSc. Thesis, Cranfield University (2005)Google Scholar
  8. 8.
    Houben, R.P.M., Allessie, M.A.: Processing of intracardiac electrograms in atrial fibrillation. IEEE Engineering in Medicine and Biology Magazine, 40–51 (November/December 2006)Google Scholar
  9. 9.
    Jacquemet, V., Oosterom, A.V., Vesin, J.V., Kappenberger, L.: Analysis of electrocardiograms during atrial fibrillation. IEEE Engineering in Medicine and Biology Magazine, 79–88 (November-December 2006)Google Scholar
  10. 10.
    Langley, P., Bourke, J.P., Murray, A.: Frequency analysis of atrial fibrillation. Computers in Cardiology, 65–68 (September 2000)Google Scholar
  11. 11.
    Moghe, S.A., Qu, F., Leonelli, F.M., Patwardhan, A.R.: Time-frequency representation of epicardial electrograms during atrial fibrillation. Biomedical Sciences Instrumentation 36, 45–50 (2000)Google Scholar
  12. 12.
    Ng, J., Kadish, A.H., Goldberger, J.J.: Effect of electrogram characteristics on the relationship of dominant frequency to atrial activation rate in atrial fibrillation. Heart Rhythm 3(11), 1295–1305 (2006)CrossRefGoogle Scholar
  13. 13.
    Ng, J., Goldberger, J.J.: Understanding and interpreting dominant frequency analysis of AF electrograms. Journal of Cardiovascular Electrophysiology 18(7), 680–685 (2007)CrossRefGoogle Scholar
  14. 14.
    Ng, J., Kadish, A.H., Goldberger, J.J.: Technical considerations for dominant frequency analysis. Journal of Cardiovascular Electrophysiology 18(7), 757–764 (2007)CrossRefGoogle Scholar
  15. 15.
    Sandberg, F., Stridth, M., Sörnmo, L.: Frequency tracking of atrial fibrillation using hidden Markov models. IEEE Transactions on Biomedical Engineering 55(2), 502–511 (2008)CrossRefGoogle Scholar
  16. 16.
    Sanders, P., Berenfeld, O., Hocini, M., Jaïs, P., Vaidyanathan, R., Hsu, L.-F., Garrigue, S., Takahashi, Y., Rotter, M., Sacher, F., Scavëe, C., Ploutz-Snyder, R., Jalife, J., Haïssaguerre, M.: Spectral analysis identifies sites of high frequency activity maintaining atrial fibrillation in humans. Circulation 112, 789–797 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Gagan Mirchandani
    • 1
  • Shruti Sharma
    • 1
  1. 1.School of Engineering, College of Engineering & Mathematical SciencesUniversity of Vermont 

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