Literature DB >> 10688321

Time-domain analysis of beat-to-beat variability of repolarization morphology in patients with ischemic cardiomyopathy.

L Burattini1, W Zareba.   

Abstract

There is growing evidence that beat-to-beat changes in ventricular repolarization contribute to increased vulnerability to ventricular arrhythmias. Beat-to-beat repolarization variability is usually measured in the electrocardiogram (ECG) by tracking consecutive QT or RT intervals. However, these measurements strongly depend on the accurate identification of T-wave endpoints, and they do not reflect changes in repolarization morphology. In this article, we propose a new computerized time-domain method to measure beat-to-beat variability of repolarization morphology without the need to identify T-wave endpoints. The repolarization correlation index (RCI) is computed for each beat to determine the difference between the morphology of repolarization within a heart-rate dependent repolarization window compared to a template (median) repolarization morphology. The repolarization variability index (RVI) describes the mean value of repolarization correlation in a studied ECG recording. To validate our method, we analyzed repolarization variability in 128-beat segments from Holter ECG recordings of 42 ischemic cardiomyopathy (ICM) patients compared to 36 healthy subjects. The ICM patients had significantly higher values of RVI than healthy subjects (in lead X: 0.045 +/- 0.035 vs. 0.024 +/- 0.010, respectively; P < .001); 18 (43%) ICM patients had RVI values above the 97.5th percentile of healthy subjects (>0.044). No significant correlation was found between the RVI values and the magnitude of heart rate, heart rate variability, QTc interval duration, or ejection fraction in studied ICM patients. In conclusion, our time-domain method, based on computation of repolarization correlation indices for consecutive beats, provides a new approach to quantify beat-to-beat variability of repolarization morphology without the need to identify T-wave endpoints.

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Year:  1999        PMID: 10688321     DOI: 10.1016/s0022-0736(99)90075-4

Source DB:  PubMed          Journal:  J Electrocardiol        ISSN: 0022-0736            Impact factor:   1.438


  7 in total

1.  Beat-to-beat repolarization variability measured by T wave spectral variance index in chronic infarcted animals.

Authors:  Esteban Raúl Valverde; Ricardo Alberto Quinteiro; Pedro David Arini; Guillermo Claudio Bertrán; Marcelo Oscar Biagetti
Journal:  Ann Noninvasive Electrocardiol       Date:  2002-10       Impact factor: 1.468

Review 2.  QT dynamics and variability.

Authors:  Wojciech Zareba; Antoni Bayes de Luna
Journal:  Ann Noninvasive Electrocardiol       Date:  2005-04       Impact factor: 1.468

3.  A class of Monte-Carlo-based statistical algorithms for efficient detection of repolarization alternans.

Authors:  Shahriar Iravanian; Uche B Kanu; David J Christini
Journal:  IEEE Trans Biomed Eng       Date:  2012-04-03       Impact factor: 4.538

4.  Predictive Power of f99 Repolarization Index for the Occurrence of Ventricular Arrhythmias.

Authors:  Corrado Giuliani; Cees A Swenne; Sumche Man; Angela Agostinelli; Sandro Fioretti; Francesco Di Nardo; Laura Burattini
Journal:  Ann Noninvasive Electrocardiol       Date:  2015-11-25       Impact factor: 1.468

Review 5.  Ventricular repolarization measures for arrhythmic risk stratification.

Authors:  Francesco Monitillo; Marta Leone; Caterina Rizzo; Andrea Passantino; Massimo Iacoviello
Journal:  World J Cardiol       Date:  2016-01-26

Review 6.  Cardiovascular effects of air pollution: what to measure in ECG?

Authors:  W Zareba; A Nomura; J P Couderc
Journal:  Environ Health Perspect       Date:  2001-08       Impact factor: 9.031

7.  Abnormal repolarization in the acute myocardial infarction patients: a frequency-based characterization.

Authors:  Corrado Giuliani; Angela Agostinelli; Sandro Fioretti; Francesco D Nardo; Laura B Burattini
Journal:  Open Biomed Eng J       Date:  2014-07-11
  7 in total

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