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OBJECTIVE Cardiovascular autonomic dysfunction is a common finding among patients with coronary artery disease (CAD) and type 2 diabetes (T2D). The reasons and prognostic value of autonomic dysfunction in CAD patients with T2D are not well known. RESEARCH DESIGN AND METHODS We examined the association between heart rate recovery (HRR), 24-h heart rate (HR) variability (SD of normal R-R interval [SDNN]), and HR turbulence (HRT), and echocardiographic parameters, metabolic, inflammatory, and coronary risk variables, exercise capacity, and the presence of T2D among 1,060 patients with CAD (mean age 67 ± 8 years; 69% males; 50% patients with T2D). Second, we investigated how autonomic function predicts a composite end point of cardiovascular death, acute coronary event, stroke, and hospitalization for heart failure during a 2-year follow-up. RESULTS In multiple linear regression model, exercise capacity was a strong predictor of HRR (R = 0.34, P < 0.001), SDNN (R = 0.33, P < 0.001), and HRT (R = 0.13, P = 0.001). In univariate analyses, a composite end point was predicted by reduced HRR (hazard ratio 1.7 [95% CI 1.1-2.6]; P = 0.020), reduced SDNN (2.0 [95% CI 1.2-3.1]; P = 0.005), and blunted HRT (2.1 [1.3-3.4]; P = 0.003) only in patients with T2D. After multivariate adjustment, none of the autonomic markers predicted the end point, but high-sensitivity C-reactive protein (hs-CRP) remained an independent predictor. CONCLUSIONS Cardiovascular autonomic function in CAD patients is associated with several variables, including exercise capacity. Autonomic dysfunction predicts short-term cardiovascular events among CAD patients with T2D, but it is not as strong an independent predictor as hs-CRP.

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This paper reviews the methods used for editing of the R-R interval time series and how this editing can influence the results of heart rate (HR) variability analyses. Measurement of HR variability from short and long-term electrocardiographic (ECG) recordings is a non-invasive method for evaluating cardiac autonomic regulation. HR variability provides information about the sympathetic-parasympathetic autonomic balance. One important clinical application is the measurement of HR variability in patients suffering from acute myocardial infarction. However, HR variability signals extracted from R-R interval time series from ambulatory ECG recordings often contain different amounts of artifact. These false beats can be either of physiological or technical origin. For instance, technical artifact may result from poorly fastened electrodes or be due to motion of the subject. Ectopic beats and atrial fibrillation are examples of physiological artifact. Since ectopic and other false beats are common in the R-R interval time series, they complicate the reliable analysis of HR variability sometimes making it impossible. In conjunction with the increased usage of HR variability analyses, several studies have confirmed the need for different approaches for handling false beats present in the R-R interval time series. The editing process for the R-R interval time series has become an integral part of these analyses. However, the published literature does not contain detailed reviews of editing methods and their impact on HR variability analyses. Several different editing and HR variability signal pre-processing methods have been introduced and tested for the artifact correction. There are several approaches available, i.e., use of methods involving deletion, interpolation or filtering systems. However, these editing methods can have different effects on HR variability measures. The effects of editing are dependent on the study setting, editing method, parameters used to assess HR variability, type of study population, and the length of R-R interval time series. The purpose of this paper is to summarize these pre-processing methods for HR variability signal, focusing especially on the editing of the R-R interval time series.

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BACKGROUND: Heart rate (HR) turbulence lasting up to 15 beats after ventricular premature beats (VPBs) may have profound effects on HR variability measures. Aim of this study was to examine the effects of HR turbulence on HR variability measures. METHODS: We developed an algorithm, which deletes 15 consecutive RR intervals after VPBs and examined the effects of the HR turbulence removal on the HR variability measures in patients after an acute myocardial infarction (AMI). Two hundred and sixty seven patients with left ventricular ejection fraction (LVEF) ≤ 0.40 and occurrence of VPBs were included in the study. Differences (%) between original HR data and HR turbulence edited data were compared. RESULTS: HR turbulence editing had variable effects on different HR variability indexes. Ultra low (ULF) and very low frequency (VLF) spectral components were mostly affected by the HR turbulence removal. Both ULF and VLF decreased significantly both at baseline Holter recordings (ULF: P = 0.006, VLF: P = 0.031) and at 6 weeks from AMI (ULF: P < 0.001, VLF: P = 0.001). The number of VPBs had a marked influence on results, e.g., when the number of VPBs exceeded the highest decile (≈50 VPBs/hour), the ULF and VLF spectral component were >30% lower after removal of turbulence. In addition, the prediction of arrhythmic events by ULF component improved after turbulence removal (AUC: 0.69 ->0.74). CONCLUSIONS: HR turbulence affects HR variability measures, especially the ULF and VFL spectral components. Editing of the HR turbulence should be considered when HR variability is measured from Holter recordings.

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BACKGROUND: Premature beats (PBs) have been considered as artifacts producing a bias in the traditional analysis of heart rate (HR) variability. We assessed the effects and significance of PBs on fractal scaling exponents in healthy subjects and patients with a recent myocardial infarction (AMI). METHODS: Artificial PBs were first generated into a time series of pure sinus beats in 20 healthy subjects and 20 post-AMI patients. Thereafter, a case-control approach was used to compare the prognostic significance of edited and nonedited fractal scaling exponents in a random elderly population and in a post-AMI population. Detrended fluctuation analysis (DFA) was used to measure the short-term (alpha1) and long-term (alpha2) fractal scaling exponents. RESULTS: Artificial PBs caused a more pronounced reduction of alpha1 value among the post-AMI patients than the healthy subjects, for example, if > 0.25% of the beats were premature a > 25% decrease in the alpha1 was observed in post-AMI patients, but 4% of the premature beats were needed to cause a 25% reduction in alpha1 in healthy subjects. Both edited (1.01 +/- 0.31 vs 1.19 +/- 0.27, P < 0.01) and unedited alpha1 (0.71 +/- 0.33 vs 0.89 +/- 0.36, P < 0.05) differed between the patients who died (n = 42) and those who survived (n = 42) after an AMI. In the general population, only unedited alpha1 differed significantly between survivors and those who died (0.96 +/- 0.19 vs 0.83 +/- 0.27, P < 0.05). CONCLUSIONS: Unedited premature beats result in an increase in the randomness of short-term R-R interval dynamics, particularly in post-AMI patients. Premature beats must not necessarily be edited when fractal analysis is used for risk stratification.

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