title: Demonstration of circadian rhythm in heart rate turbulence using novel application of correlator functions.
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(Deltat) = <TS(t) TS(t + Deltat)> then was calculated and averaged over all patients. Positive Corr(Deltat) indicates that TS at a given hour and Deltat hours later are similar. TO was treated likewise. Simulations and mathematical analysis showed that a circadian rhythm required Corr(Deltat) to have a U-shape consisting of positive values near Deltat = 0 and 23 and negative values for intermediate Deltat. Significant deviation of Corr(Deltat) 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 approximately 10 in CAST (both P <.0001) and approximately 3 in ISAR (both P <.0001), indicating the presence of circadian rhythmicity.
  
  
  CONCLUSIONS
  Correlator functions may be a powerful tool for detecting the presence of circadian rhythms in noisy data, even with recordings limited to 24 hours.
authors.affiliation: Department of Internal Medicine, St. Louis University, St. Louis, Missouri 63110, USA. watanabe@slu.edu
authors.forename: Mari A
authors.initials: MA
authors.lastname: Watanabe
authors.forename: Mark
authors.initials: M
authors.lastname: Alford
authors.forename: Raphael
authors.initials: R
authors.lastname: Schneider
authors.forename: Axel
authors.initials: A
authors.lastname: Bauer
authors.forename: Petra
authors.initials: P
authors.lastname: Barthel
authors.forename: Phyllis K
authors.initials: PK
authors.lastname: Stein
authors.forename: Georg
authors.initials: G
authors.lastname: Schmidt
mesh_terms: D000319:Adrenergic beta-Antagonists / Q000627:therapeutic use; D016009:Chi-Square Distribution; D002940:Circadian Rhythm / Q000187:drug effects; D016208:Databases, Factual; D015716:Electrocardiography, Ambulatory; D005260:Female; D006329:Heart Conduction System / Q000187:drug effects / Q000503:physiopathology; D006339:Heart Rate / Q000187:drug effects; D006801:Humans; D008297:Male; D008432:Mathematical Computing; D008875:Middle Aged; D009203:Myocardial Infarction / Q000188:drug therapy / Q000503:physiopathology; D011237:Predictive Value of Tests; D012815:Signal Processing, Computer-Assisted; D018879:Ventricular Premature Complexes / Q000503:physiopathology
chemical_list: D000319:Adrenergic beta-Antagonists