Location of Repository

Comparison of computation time for estimation of dominant frequency of atrial electrograms: Fast fourier transform, blackman tukey, autoregressive and multiple signal classification

By Anita Ahmad, Fernando Soares Schlindwein and G. André Ng

Abstract

This is the publisher's PDF of the paper published as Journal of Biomedical Science and Engineering, 2010, 3(9), pp.843-847 (scirp©2010). The published version is also available from the publisher's website: http://www.scirp.org/journal/jbise/\ud DOI: 10.4236/jbise.2010.39114Dominant frequency (DF) of electrophysiological data is an effective approach to estimate the activation rate during Atrial Fibrillation (AF) and it is important to understand the pathophysiology of AF and to help select candidate sites for ablation. Frequency analysis is used to find and track DF. It is important to minimize the catheter insertion time in the atria as it contributes to the risk for the patients during this procedure, so DF estimation needs to be obtained as quickly as possible. A comparison of computation tim- es taken for spectrum estimation analysis is presented in this paper. Fast Fourier Transform (FFT), Blackman-Tukey (BT), Autoregressive (AR) and Multiple Signal Classification (MUSIC) methods are used to obtain the frequency spectrum of the signals. The time to produce DF was measured for each method. The method which takes the shortest time for analysis is selected for real time application purpose

Publisher: Scientific Research Publishing
Year: 2010
DOI identifier: 10.4236/jbise.2010.39114
OAI identifier: oai:lra.le.ac.uk:2381/8616

Suggested articles

Preview

Citations

  1. (2008). Advanced digital signal processing and noise reduction, 3rd Edition, doi
  2. (2007). Analysis of the bending fracture process for piezoelectric composite actuators using dominant frequency bands by acoustic emission. doi
  3. (2009). Atrial fibrillation ablation: Who comes into consideration? Herzschrittmachertherapie und Elektrophysiologie, doi
  4. (1979). Digital methods for signal analysis, doi
  5. (2002). Digital signal processing:A practical approach. 2nd Edition,
  6. (2009). Do ‘Dominant Frequencies’ explain the listener’s response to formant and spectrum shape variations? doi
  7. (2009). Femoral vascular complications following catheter ablation of atrial fibrillation. doi
  8. (2008). Modeling atrial arrhythmias: Impact on clinical diagnosis and therapies. doi
  9. (1988). Modern spectral estimation theory & application,
  10. (2003). Mortality, morbidity, and quality of life after circumferential pulmonary vein ablation for atrial fibrillation: Outcomes from a controlled nonrandomized long term study. doi
  11. (1979). Multiple emitter location and signal parameter estimation. doi
  12. (2007). Quantifying activation frequency in Copyright © doi
  13. S.A.and Crijns, HJ et al (2007). HRS/EHRA/ECAS expert consensus statement on catheter and surgical ablation of atrial fibrillation: Recommendations for personnel, policy, procedures and follow-up. doi
  14. (1998). Spatiotemporal periodicity during atrial fibrillation in the isolated sheep heart. doi
  15. (2000). Stable microreentrant sources as a mechanism of atrial fibrillation in the isolated sheep heart. doi
  16. (1996). Statistical digital signal processing and modeling, doi
  17. (1973). The retrieval of harmonics from a covariance function. doi
  18. (1967). The use of fast fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. doi
  19. (2007). Understanding and interpreting dominant frequency analysis of AF electrograms, doi
  20. (2005). Worldwide survey on the methods, efficacy, and safety of catheter ablation for human atrial fibrillation. doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.