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. 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.
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. 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.
Authors: Mari A Watanabe; Rajeshkumar Bhalodia; Eric J Lundequam; Peter P Domitrovich; Brian C Steinmeyer; Phyllis K Stein; Kenneth E Freedland; Stephen P Duntley; Robert M Carney Journal: Indian Pacing Electrophysiol J Date: 2008-11-01