利用关联函数的新应用证明心率Turbulence的昼夜节律
Demonstration of Circadian Rhythm in Heart Rate Turbulence using Novel Application of Correlator Functions
摘要：
背景资料：湍流起始（TO）和湍流斜率（TS）这两个心率湍流参数一直难以反映昼夜节律。目的：设计一种新的方法来检测噪声数据中的昼夜节律，并将其应用于两个心肌梗死后数据库，心律失常抑制试验（CAST，n=684）和心律失常风险的创新分层（ISAR，n=327）的选择Holter记录。方法：对于每例患者，计算每小时的TS和TO，其中VPC>4。然后计算自相关函数Corr（Δ t）= <TS（t）TS（t+ Δ t）>，并对所有患者取平均值。正相关（Δ t）表示给定小时的TS和Δ t小时后的TS相似。TO也受到了同样的待遇。模拟和数学分析表明，昼夜节律要求Corr（Delta t）具有U形，由Delta t=0和23附近的正值和中间Delta t的负值组成。Corr（Δ t）与纯噪声的关联函数函数的显著偏差被评估为卡方值。结果：昼夜节律模式不明显，每小时平均TS和TO绘制时钟时间，其中有大的误差条。然而，他们的关联函数函数在CAST中产生的卡方值约为10（均为p<0.0001），在ISAR中产生的卡方值约为3（均为p<0.0001），表明存在昼夜节律。结论：关联函数可以是用于检测噪声数据中昼夜节律的存在的强大工具，即使记录限于24小时。
英文摘要：
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.
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