Demonstration of Circadian Rhythm in Heart Rate Turbulence using Novel Application of Correlator Functions

Abstract

Background: It has been difficult to demonstrate circadian rhythm in the two parameters of heart rate turbulence, turbulence onset (TO) and turbulence slope (TS). Objective: To devise a new method for detecting circadian rhythm in noisy data, and apply it to selected Holter recordings from two post-myocardial 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 VPCs. An autocorrelation function Corr(Delta t) = <TS(t) TS(t+Delta t)> was then calculated, and averaged over all patients. Positive Corr(Delta t) indicates that TS at a given hour and Delta t hours later are similar. TO was treated likewise. Simulations and mathematical analysis showed that circadian rhythm required Corr(Delta t) to have a U-shape consisting of positive values near Delta t=0 and 23, and negative values for intermediate Delta t. Significant deviation of Corr(Delta t) from the correlator function of pure noise was evaluated as a chi-squared value. Results: Circadian patterns were not apparent in hourly averages of TS and TO plotted against clock time, which had large error bars. Their correlator functions, however, produced chi-squared values of ~10 in CAST (both p<0.0001) and ~3 in ISAR (both p<0.0001), indicating presence of circadian rhythmicity. Conclusion: Correlator functions may be a powerful tool for detecting presence of circadian rhythms in noisy data, even with recordings limited to 24 hours.