Literature DB >> 21208232

Frequency domain and time complex analyses manifest low correlation and temporal variability when calculating activation rates in atrial fibrillation patients.

Angelo B Biviano1, James Coromilas, Edward J Ciaccio, William Whang, Kathleen Hickey, Hasan Garan.   

Abstract

BACKGROUND: Atrial fibrillation (AF) activation rates have been calculated using both frequency domain and time complex analyses. Direct comparisons of these methods are limited. We report: (1) their correlation when measuring AF activation rates, (2) comparisons of recording durations required to minimize variability, and (3) differences in the temporal reproducibility.
METHODS: AF activation rates were calculated using domain frequency (DF) (via fast Fourier transform) and time complex (TC) (via beat-to-beat activation measurements) analyses. We compared: (1) AF frequencies derived from each method; (2) successively longer subinterval durations to their 16-second reference intervals, and (3) the correlation between consecutively collected 8-second segments and segments collected 10 minutes apart.
RESULTS: There was low intraclass correlation coefficient (ICC = 0.234) when comparing AF activation rates derived using DF versus TC analysis. There was no difference in the frequencies between any of the subintervals compared to their 16-second reference intervals, but variability of measurements was higher for intervals <8 seconds (P < 0.01). Correlations between successive segments and segments taken 10 minutes apart were 0.92 and 0.75 using DF analysis (P < 0.001), and 0.72 and 0.49 using TC analysis (P < 0.001).
CONCLUSIONS: There is low correlation between the DF and TC methods of analyzing AF activation rates. While AF rates do not differ between subintervals and 16-second reference electrograms, the variability of measurements is dependent upon the subinterval duration, and increases for durations less than 8 seconds. AF rates were prone to change over a 10-minute time period. These results point out existing clinical limitations of measuring atrial activation rates in AF patients. ©2010, The Authors. Journal compilation ©2010 Wiley Periodicals, Inc.

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Year:  2011        PMID: 21208232     DOI: 10.1111/j.1540-8159.2010.02993.x

Source DB:  PubMed          Journal:  Pacing Clin Electrophysiol        ISSN: 0147-8389            Impact factor:   1.976


  6 in total

1.  Atrial electromechanical cycle length mapping in paced canine hearts in vivo.

Authors:  Alexandre Costet; Ethan Bunting; Julien Grondin; Alok Gambhir; Elisa E Konofagou
Journal:  IEEE Trans Ultrason Ferroelectr Freq Control       Date:  2015-07       Impact factor: 2.725

2.  Temporal stability in the spectral representation of complex fractionated atrial electrograms.

Authors:  Edward J Ciaccio; Angelo B Biviano; Alok Gambhir; Jason T Jacobson; Hasan Garan
Journal:  Pacing Clin Electrophysiol       Date:  2013-08-26       Impact factor: 1.976

3.  Improved frequency resolution for characterization of complex fractionated atrial electrograms.

Authors:  Edward J Ciaccio; Angelo B Biviano; William Whang; Hasan Garan
Journal:  Biomed Eng Online       Date:  2012-04-03       Impact factor: 2.819

4.  A new transform for the analysis of complex fractionated atrial electrograms.

Authors:  Edward J Ciaccio; Angelo B Biviano; William Whang; James Coromilas; Hasan Garan
Journal:  Biomed Eng Online       Date:  2011-05-12       Impact factor: 2.819

5.  Identification of recurring patterns in fractionated atrial electrograms using new transform coefficients.

Authors:  Edward J Ciaccio; Angelo B Biviano; William Whang; Hasan Garan
Journal:  Biomed Eng Online       Date:  2012-01-19       Impact factor: 2.819

6.  Software algorithm and hardware design for real-time implementation of new spectral estimator.

Authors:  Edward J Ciaccio; Angelo B Biviano; Hasan Garan
Journal:  Biomed Eng Online       Date:  2014-05-09       Impact factor: 2.819

  6 in total

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