Mathias Baumert

Methods derived from nonlinear dynamics for analysing heart rate variability

Methods from nonlinear dynamics (NLD) have shown new insights into heart rate (HR) variability changes under various physiological and pathological conditions, providing additional prognostic information and complementing traditional time- and frequency-domain analyses. In this review, some of the most prominent indices of nonlinear and fractal dynamics are summarized and their algorithmic implementations and applications in clinical trials are discussed. Several of those indices have been proven to be of diagnostic relevance or have contributed to risk stratification. In particular, techniques based on mono- and multifractal analyses and symbolic dynamics have been successfully applied to clinical studies. Further advances in HR variability analysis are expected through multidimensional and multivariate assessments. Today, the question is no longer about whether or not methods from NLD should be applied; however, it is relevant to ask which of the methods should be selected and under which basic and standardized conditions should they be applied.

Heart Rate Variability, Blood Pressure Variability, and Baroreflex Sensitivity in Overtrained Athletes

Objective:To assess the effects of abruptly intensified physical training on cardiovascular control. Design:Retrospective longitudinal study. Setting:Research laboratory. Participants:Ten healthy athletes (5 men and 5 women) from track and field as well as triathlon. Interventions:A 2-week training camp, including daily stepwise increasing cycling tests, running of 40 minutes, and additional cycling of 60 minutes. Main Outcome Measurements:Time and frequency domain parameters of resting heart rate and blood pressure variability (HRV and BPV) and baroreflex sensitivity (BRS), before, during, and after the training camp. Results:We found significantly reduced HRV during the training camp (mean beat-to-beat interval: 1042 [937 to 1194] ms vs. 933 [832 to 1103] ms vs. 1055 [947 to 1183] ms, P < 0.01; root-mean-square of beat-to-beat interval differences: 68 [52 to 95] ms vs. 52 [38 to 71] ms vs. 61 [48 to 78] ms, P < 0.05). Further, BRS was significantly reduced: 25.2 (20.4 to 40.4) ms/mmHg vs. 17.0 (12.9 to 25.7) ms/mmHg vs. 25.7 (18.8 to 29.1) ms/mmHg, P < 0.05. These effects disappeared at a large degree after 3 to 4 days of recovery. Conclusion:Abruptly intensified physical training results in an altered autonomic cardiovascular activity towards parasympathetic inhibition and sympathetic activation that can be monitored by means of HRV and BRS analyses and might provide useful markers to avoid the overtraining syndrome.

Joint symbolic dynamic analysis of beat-to-beat interactions of heart rate and systolic blood pressure in normal pregnancy.

Pregnancy induces important changes in the autonomic control. Measures of heart rate (HR) variability and systolic blood pressure (SP) variability are sensitive to those changes. The interactions between HR and SP are complex and strongly non-linear. Therefore they cannot be completely described by linear analysis techniques. A study of joint symbolic dynamics is presented as a new short-term non-linear analysis method to investigate the interactions between HR and SP. Continuous, non-invasive 30 min blood pressure recordings (Portapres) of 25 pregnant and 14 non-pregnant women were analysed. Time series of beat-to-beat HR and SP were extracted. Using the concept of joint symbolic dynamics, HR and SP changes were transformed into a bivariate symbol vector. Subsequently, this symbol vector was transformed into a word series (words consisting of three successive symbols), and the probability of occurrence of each word type was calculated and compared between both groups. Significant differences were found in five word types between pregnant and non-pregnant women: w0,4(0.021±0.011 against 0.008±0.006; p=0.022), w4,6(0.020±0.010 against 0.007±0.003; p=0.001), w3,2(0.004±0.003 against 0.007±0.003; p=0.038), w6,5(0.009±0.007 against 0.023±0.008; p<0.001) and w3,6(0.011±0.007 against 0.023±0.008; p=0.001). Joint symbolic dynamics provides an efficient non-linear representation of HR and SP interactions that offers simple physiological interpretations.

Short-term heart rate complexity is reduced in patients with type 1 diabetes mellitus☆

OBJECTIVE The aim of this study was to test whether new heart rate variability (HRV) complexity measures provide diagnostic information regarding early subclinical autonomic dysfunction in diabetes mellitus (DM). METHODS HRV in DM type 1 patients (n=17, 10f, 7m) aged 12.9-31.5 years (duration of DM 12.4+/-1.2 years) was compared to a control group of 17 healthy matched probands. The length of R-R intervals was measured over 1h using a telemetric ECG system. In addition to linear measures, we assessed HRV complexity measures, including multiscale entropy (MSE), compression entropy and various symbolic dynamic measures (Shannon and Renyi entropies, normalized complexity index (NCI), and pattern classification). RESULTS HRV magnitude was significantly reduced in patients with DM. Several HRV complexity parameters (MSE at scales 2-4, Renyi entropy, NCI) were also significantly reduced in diabetics. MSE indices and compression entropy did not correlate with linear measures. CONCLUSIONS The magnitude and complexity of HRV are reduced in young patients with DM, indicating vagal dysfunction. SIGNIFICANCE The quantification of HRV complexity in combination with its magnitude may provide an improved diagnostic tool for cardiovascular autonomic neuropathy in DM.

Bipolar Electrogram Shannon Entropy at Sites of Rotational Activation: Implications for Ablation of Atrial Fibrillation

Background—The pivot is critical to rotors postulated to maintain atrial fibrillation (AF). We reasoned that wavefronts circling the pivot should broaden the amplitude distribution of bipolar electrograms because of directional information encoded in these signals. We aimed to determine whether Shannon entropy (ShEn), a measure of signal amplitude distribution, could differentiate the pivot from surrounding peripheral regions and thereby assist clinical rotor mapping. Methods and Results—Bipolar electrogram recordings were studied in 4 systems: (1) computer simulations of rotors in a 2-dimensional atrial sheet; (2) isolated rat atria recorded with a multi-electrode array (n=12); (3) epicardial plaque recordings of induced AF in hypertensive sheep (n=11); and (4) persistent AF patients (n=10). In the model systems, rotation episodes were identified, and ShEn calculated as an index of amplitude distribution. In humans, ShEn distribution was analyzed at AF termination sites and with respect to complex fractionated electrogram mean. We analyzed rotation episodes in simulations (4 cycles) and animals (rats: 14 rotors, duration 80±81 cycles; sheep: 13 rotors, 4.2±1.5 cycles). The maximum ShEn bipole was consistently colocated with the pivot zone. ShEn was negatively associated with distance from the pivot zone in simulated spiral waves, rats, and sheep. ShEn was modestly inversely associated with complex fractionated electrogram; however, there was no relationship at the sites of highest ShEn. Conclusions—ShEn is a mechanistically based tool that may assist AF rotor mapping.Background— The pivot is critical to rotors postulated to maintain atrial fibrillation (AF). We reasoned that wavefronts circling the pivot should broaden the amplitude distribution of bipolar electrograms because of directional information encoded in these signals. We aimed to determine whether Shannon entropy (ShEn), a measure of signal amplitude distribution, could differentiate the pivot from surrounding peripheral regions and thereby assist clinical rotor mapping. Methods and Results— Bipolar electrogram recordings were studied in 4 systems: (1) computer simulations of rotors in a 2-dimensional atrial sheet; (2) isolated rat atria recorded with a multi-electrode array (n=12); (3) epicardial plaque recordings of induced AF in hypertensive sheep (n=11); and (4) persistent AF patients (n=10). In the model systems, rotation episodes were identified, and ShEn calculated as an index of amplitude distribution. In humans, ShEn distribution was analyzed at AF termination sites and with respect to complex fractionated electrogram mean. We analyzed rotation episodes in simulations (4 cycles) and animals (rats: 14 rotors, duration 80±81 cycles; sheep: 13 rotors, 4.2±1.5 cycles). The maximum ShEn bipole was consistently colocated with the pivot zone. ShEn was negatively associated with distance from the pivot zone in simulated spiral waves, rats, and sheep. ShEn was modestly inversely associated with complex fractionated electrogram; however, there was no relationship at the sites of highest ShEn. Conclusions— ShEn is a mechanistically based tool that may assist AF rotor mapping.

Relation between QT interval variability and cardiac sympathetic activity in hypertension

Elevated QT interval variability is a predictor of malignant ventricular arrhythmia, but the underlying mechanisms are incompletely understood. A recent study in dogs with pacing-induced heart failure suggests that QT variability is linked to cardiac sympathetic nerve activity. The aim of this study was to determine whether increased cardiac sympathetic activity is associated with increased beat-to-beat QT interval variability in patients with essential hypertension. We recorded resting norepinephrine (NE) spillover into the coronary sinus and single-lead, short-term, high-resolution, body-surface ECG in 23 patients with essential hypertension and 9 normotensive control subjects. To assess beat-to-beat QT interval variability, we calculated the overall QT variability (QTVN) as well as the QT variability index (QTVi). Cardiac NE spillover (12.2 ± 6.5 vs. 20.7 ± 14.7, P = 0.03) and QTVi (-1.75 ± 0.36 vs. -1.42 ± 0.50, P = 0.05) were significantly increased in hypertensive patients compared with normotensive subjects. QTVN was significantly correlated with cardiac NE spillover (r(2) = 0.31, P = 0.001), with RR variability (r(2) = 0.20, P = 0.008), and with systolic blood pressure (r(2) = 0.16, P = 0.02). Linear regression analysis identified the former two as independent predictors of QTVN. In conclusion, elevated repolarization lability is directly associated with sympathetic cardiac activation in patients with essential hypertension.

Short-term heart rate variability and cardiac norepinephrine spillover in patients with depression and panic disorder

Changes in measures of heart rate variability (HRV) have been associated with an increased risk for sudden cardiac death. The mechanisms underlying this association are not known. The objective of this study was to assess the relationship between the amount of norepinephrine (NE) released from the cardiac sympathetic terminals and short-term HRV. The study comprised 8 healthy subjects, 12 patients with major depression, and 7 patients with panic disorder. Cardiac NE spillover was determined using direct coronary sinus blood sampling coupled with an NE isotope dilution methodology. Short-term HRV was quantified using detrended fluctuation analysis, symbolic dynamics, sample entropy, and standard time and frequency domain measures. Neither HRV nor cardiac NE spillover was significantly different between the analyzed groups. None of the standard HRV metrics was significantly correlated with cardiac NE spillover, but there was a moderate correlation between two complexity measures of HRV (symbolic dynamics) and cardiac NE spillover (patterns with 2 like variations, r = -0.37 and P = 0.05; and patterns with no variations: r = 0.34 and P = 0.06). In conclusion, there is no correlation between standard HRV measures and cardiac NE spillover in humans. Short-term complexity of heart rate is only moderately affected by sympathetic neural outflow. Therefore, the predictive value of most HRV measures for sudden cardiac death may predominantly result from their capacity to capture vagally mediated heart rate modulations.

QT interval variability and cardiac norepinephrine spillover in patients with depression and panic disorder

Suggestions were made that increased myocardial sympathetic activity is reflected by elevated QT variability (dynamic changes in QT interval duration). However, the relationship between QT variability and the amount of norepinephrine released from the cardiac sympathetic terminals is unknown. We thus attempted to assess this relationship. The study was performed in 17 subjects (12 with major depressive disorder and 5 with panic disorder). Cardiac norepinephrine spillover (measured by direct catheter technique coupled with norepinephrine isotope dilution methodology) was assessed before and 4 mo after treatment with selective serotonin reuptake inhibitor (SSRI) antidepressants. The distribution of the cardiac norepinephrine spillover was bimodal, with the majority of patients having values of < or =10 ng/min. There was a positive correlation between cardiac norepinephrine spillover and corrected QT interval (r = 0.7, P = 0.03) but not with any of the QT variability measures. However, in a subgroup of five patients who had high levels of cardiac norepinephrine spillover (>20 ng/min) a tendency for a strong positive correlation with variance of QT intervals (r = 0.9, P = 0.08) was observed. There were significant correlations between the severity of depression and QT variability indexes normalized to the heart rate [QTVi and QT interval/R-R interval (QT/RR) coherence] and between the severity of anxiety and the QT/RR residual and regression coefficient, respectively. Treatment with SSRI antidepressants substantially reduced depression score but did not affect any of the QT variability indexes. We conclude that in depression/panic disorder patients with near-normal cardiac norepinephrine levels QT variability is not correlated with cardiac norepinephrine spillover and is not affected by treatment with SSRI.

The effect of orthostatic stress on multiscale entropy of heart rate and blood pressure

Cardiovascular control acts over multiple time scales, which introduces a significant amount of complexity to heart rate and blood pressure time series. Multiscale entropy (MSE) analysis has been developed to quantify the complexity of a time series over multiple time scales. In previous studies, MSE analyses identified impaired cardiovascular control and increased cardiovascular risk in various pathological conditions. Despite the increasing acceptance of the MSE technique in clinical research, information underpinning the involvement of the autonomic nervous system in the MSE of heart rate and blood pressure is lacking. The objective of this study is to investigate the effect of orthostatic challenge on the MSE of heart rate and blood pressure variability (HRV, BPV) and the correlation between MSE (complexity measures) and traditional linear (time and frequency domain) measures. MSE analysis of HRV and BPV was performed in 28 healthy young subjects on 1000 consecutive heart beats in the supine and standing positions. Sample entropy values were assessed on scales of 1-10. We found that MSE of heart rate and blood pressure signals is sensitive to changes in autonomic balance caused by postural change from the supine to the standing position. The effect of orthostatic challenge on heart rate and blood pressure complexity depended on the time scale under investigation. Entropy values did not correlate with the mean values of heart rate and blood pressure and showed only weak correlations with linear HRV and BPV measures. In conclusion, the MSE analysis of heart rate and blood pressure provides a sensitive tool to detect changes in autonomic balance as induced by postural change.

QT interval variability in body surface ECG : measurement, physiological basis, and clinical value: position statement and consensus guidance endorsed by the European Heart Rhythm Association jointly with the ESC Working Group on Cardiac Cellular Electrophysiology

This consensus guideline discusses the electrocardiographic phenomenon of beat-to-beat QT interval variability (QTV) on surface electrocardiograms. The text covers measurement principles, physiological basis, and clinical value of QTV. Technical considerations include QT interval measurement and the relation between QTV and heart rate variability. Research frontiers of QTV include understanding of QTV physiology, systematic evaluation of the link between QTV and direct measures of neural activity, modelling of the QTV dependence on the variability of other physiological variables, distinction between QTV and general T wave shape variability, and assessing of the QTV utility for guiding therapy. Increased QTV appears to be a risk marker of arrhythmic and cardiovascular death. It remains to be established whether it can guide therapy alone or in combination with other risk factors. QT interval variability has a possible role in non-invasive assessment of tonic sympathetic activity.