Interbeat Interval Time Series Research Articles

The time-course of electrocardiographic interbeat interval dynamics in alcoholic subjects after short-term abstinence

Alcohol dependence has been correlated with decreases in heart rate variability. However, the time course of recovery of heart rate variability after cessation of alcohol consumption is unknown. We used electrocardiogram (ECG) data serially obtained from a population of detoxifying alcoholic subjects to determine the Hurst exponent of the ECG interbeat interval time series. Higher values of the Hurst exponent are associated with decreased heart rate variability when H≤0.5. We tested a series of response-surface models relating the Hurst exponent ( H) thus obtained to the following independent variables: the time interval T (days since last use of alcohol), A (age in years at time of admission), and gender. The best-fit model was: H( T)=( KA+ H m T+ H f T)/(1+ T), F=5.2, P( F)≤0.01. Model parameters were: K=0.008±0.002 (mean±SEM); asymptotic H-values for males and females: H m=0.24±0.02 and H f=0.16±0.03, respectively, significantly different at P≤0.05. Age was the strongest predictor of initial H-values in this alcoholic population sample.

Behavioral-independent features of complex heartbeat dynamics.

We test whether the complexity of the cardiac interbeat interval time series is simply a consequence of the wide range of scales characterizing human behavior, especially physical activity, by analyzing data taken from healthy adult subjects under three conditions with controls: (i) a "constant routine" protocol where physical activity and postural changes are kept to a minimum, (ii) sympathetic blockade, and (iii) parasympathetic blockade. We find that when fluctuations in physical activity and other behavioral modifiers are minimized, a remarkable level of complexity of heartbeat dynamics remains, while for neuroautonomic blockade the multifractal complexity decreases.

Ondansetron modulates pharmacodynamic effects of ketamine on electrocardiographic signals in rhesus monkeys

Electrocardiographic signal dynamics were examined in rhesus monkeys ( Macaca mulatta) before and after treatment with ketamine and/or ondansetron. Ketamine exerts differential pharmacodynamic effects on behavior in animals stratified according to a measure of central serotonergic turnover. We hypothesized that measures of serotonergic turnover might explain some of the variance in the electrocardiographic (ECG) response to ketamine. Electrocardiographic recordings of animals were obtained at baseline, after administration of either saline or ondansetron (0.125 mg/kg), and after administration of ketamine (15 mg/kg). Electrocardiographic signal dynamics were measured using an algorithm that extracts the Hurst parameter ( H) of the interbeat interval (IBI) time-series. H decreased after ketamine administration, (mean±S.E.M.), 0.33±0.04 vs. 0.12±0.02, P≤0.001, n=10. Cerebrospinal fluid 5-hydroxyindole-3-acetic acid (5-HIAA) concentrations, a measure of serotonergic turnover, predicted the monkeys' response to ketamine, H=0.001 (5-HIAA, pmol/ml)-0.130, R=0.66, P≤0.003, n=18. Ondansetron attenuated the response to ketamine, 0.14±0.02 vs. 0.08±0.02, P≤0.05, n=8, ondansetron vs. saline. These data provide evidence that naturally occurring differences in serotonin function alter the ECG response of the animals to ketamine and that activation of the serotonin type-3 receptor by ketamine is involved.

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

Anxiety and autonomic flexibility: a cardiovascular approach

Autonomic characteristics of panickers, blood phobics, and nonanxious controls were compared with a variety of cardiovascular measures, including spectral analysis of the cardiac inter-beat interval time series (derived from the electrocardiogram). Responses to laboratory stressors (shock avoidance and cold face stress) of 16 participants who reported recent occurrences of frequent severe panic attacks, 15 participants who reported strong somatic reactions and fainting to the sight of blood, and 15 controls, were recorded. Results suggested distinct autonomic patterns among the three groups. Across conditions, panickers displayed the highest heart rates (HR) coupled with the least HR variability, which indicates low levels of cardiac vagal tone. Blood phobics showed more vagally mediated HR variability than panickers, with a significant association between cardiac rate and mean arterial pressure. Controls generally showed the most HR variability and `spectral reserve' (a quality that indicates flexible responsivity). Results are discussed in the context of traditional models of anxiety and autonomic activity in contrast to contemporary notions of stability and change in biological systems.

Complexity and Predictability of the Heartbeat Time Series in Transplanted Subjects

Physiological fluctuations in heart rate, known as "sinus arrhythmia", are due to the influence of the autonomic nervous system on the sinus node, modulated by baroreceptor activity and respiration. Reduction in heart rate variability (HRV) correlated to age or disease has been reported by several investigators, together with its importance as prognostic indicator in pathophysiological situations. In this paper the heart rhythm is investigated in heart transplanted subjects, an apparently opposite condition with respect to normal hearts as for the influence of the autonomic system. Selected segments of the (first difference) interbeat interval time series are analyzed with an efficient approach [Sugihara, 1994] able to evaluate both the short term predictability and the nonlinearity of these data. Moreover, other more qualitative methods are used to better characterize the experimental sequences. The results indicate that these time series only occasionally exhibit some degree of predictability, as assessed by this metric. A careful examination of the predictability behavior, by means of surrogate data, reveals that it cannot be interpreted as evidence of nonlinearity. Rather, a stochastic-like dynamics seems to characterize the transplanted hearts.

Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics.

We postulated that aging is associated with disruption in the fractallike long-range correlations that characterize healthy sinus rhythm cardiac interval dynamics. Ten young (21-34 yr) and 10 elderly (68-81 yr) rigorously screened healthy subjects underwent 120 min of continuous supine resting electrocardiographic recording. We analyzed the interbeat interval time series using standard time and frequency domain statistics and using a fractal measure, detrended fluctuation analysis, to quantify long-range correlation properties. In healthy young subjects, interbeat intervals demonstrated fractal scaling, with scaling exponents (alpha) from the fluctuation analysis close to a value of 1.0. In the group of healthy elderly subjects, the interbeat interval time series had two scaling regions. Over the short range, interbeat interval fluctuations resembled a random walk process (Brownian noise, alpha = 1.5), whereas over the longer range they resembled white noise (alpha = 0.5). Short (alpha s)- and long-range (alpha 1) scaling exponents were significantly different in the elderly subjects compared with young (alpha s = 1.12 +/- 0.19 vs. 0.90 +/- 0.14, respectively, P = 0.009; alpha 1 = 0.75 +/- 0.17 vs. 0.99 +/- 0.10, respectively, P = 0.002). The crossover behavior from one scaling region to another could be modeled as a first-order autoregressive process, which closely fit the data from four elderly subjects. This implies that a single characteristic time scale may be dominating heartbeat control in these subjects. The age-related loss of fractal organization in heartbeat dynamics may reflect the degradation of integrated physiological regulatory systems and may impair an individual's ability to adapt to stress.