Demonstration of circadian rhythm in heart rate turbulence using novel application of correlator functions

Mari A. Watanabe, Mark Alford, Raphael Schneider, Axel Bauer, Petra Barthel, Phyllis K. Stein, Georg Schmidt

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Background: Demonstration of a circadian rhythm in two parameters of heart rate turbulence-turbulence onset (TO) and turbulence slope (TS)-has been difficult. Objective: The aim of this study was to devise a new method for detecting circadian rhythm in noisy data and to apply it to selected Holter recordings from two postmyocardial infarction databases: Cardiac Arrhythmia Suppression Trial (CAST, n = 684) and Innovative Stratification of Arrhythmic Risk (ISAR, n = 327). Methods: For each patient, TS and TO were calculated for each hour with >4 ventricular premature contractions (VPCs). An autocorrelation function Corr(Δt) = <TS(t) TS(t + Δt)> then was calculated and averaged over all patients. Positive Corr(Δt) indicates that TS at a given hour and Δt hours later are similar. TO was treated likewise. Simulations and mathematical analysis showed that a circadian rhythm required Corr(Δt) to have a U-shape consisting of positive values near Δt = 0 and 23 and negative values for intermediate Δt. Significant deviation of Corr(Δt) from the correlator function of pure noise was evaluated as a Chi-square value. Results: Circadian patterns were not apparent in hourly averages of TS and TO plotted against clock time, which had large error bars. However, their correlator functions produced Chi-square values of ∼10 in CAST (both P <.0001) and ∼3 in ISAR (both P <.0001), indicating the presence of circadian rhythmicity. Conclusion: Correlator functions may be a powerful tool for detecting the presence of circadian rhythms in noisy data, even with recordings limited to 24 hours.

Original languageEnglish
Pages (from-to)292-300
Number of pages9
JournalHeart rhythm
Volume4
Issue number3
DOIs
StatePublished - Mar 2007

Keywords

  • Autocorrelation
  • Circadian rhythm
  • Heart rate turbulence
  • Statistical analysis

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