Cardiac Time Series Research Articles

Haemodynamic adaptation during sudden gravity transitions

Haemodynamic responses during parabolic flight were studied. The hypothesis that haemodynamic changes may be counteracted by a transient vagal reflex during acute gravity transitions was tested. ECG, arterial pressure and respiration were recorded continuously in seven male subjects during parabolic flight. Beat-to-beat haemodynamic parameters were estimated. In the supine position no significant differences were shown among the different gravity phases. In the upright position, significant within-group differences were observed across gravity phases for all parameters. Postural differences in haemodynamic data disappeared during the microgravity phase and were enlarged during hypergravity phases. Detailed temporal analysis of cardiac time series in standing subjects confirmed the hypothesized biphasic response of initial parasympathetic modulation: a sharp increase of RRI within 3-5 s followed by a 10% decrease in the remaining period of microgravity (p < 0.001); a sharp increase in SAP within 2-4 s followed by a slow decrease of 25%. Significant within-group differences were observed in the standing position for mean RRI (836 ± 170 ms, p = 0.003), DAP (66 ± 8 mmHg, p < 0.001), MAP (139 ± 12 mmHg, p = 0.001), RRI HF amplitude (17.6 ± 7.5 ms, p < 0.001), SV (146 ± 5%, p < 0.001) and SVR (73 ± 10%, p = 0.020). In standing subjects, the initial baroreflex-mediated vagal heart rate response is limited to a transition period at early microgravity lasting about 3-5 s, followed by a gradual heart rate recovery during the remaining 15-17 s due to a parasympathetic withdrawal. The resultant increase in cardiac output induces a baroreflex-mediated systemic vasodilatation, which may be the driving force for a decreased arterial pressure in weightlessness.

Detection of changes in the fractal scaling of heart rate and speed in a marathon race

The aim of this study was to detect changes in the fractal scaling behavior of heart rate and speed fluctuations when the average runner’s speed decreased with fatigue. Scaling analysis in heart rate (HR) and speed (S) dynamics of marathon runners was performed using the detrended fluctuation analysis (DFA) and the wavelet based structure function. We considered both: the short-range ( α 1 ) and the long-range ( α 2 ) scaling exponents for the DFA method separated by a change-point, n 0 = 64 = 5.3 min (box length), the same for all the races. The variability of HR and S decreased in the second part of the marathon race, while the cardiac cost time series (i.e. the number of cardiac beats per meter) increased due to the decreasing speed behavior. The scaling exponents α 1 and α 2 of HR and α 1 of S, increased during the race ( p < 0.01 ) as did the HR wavelet scaling exponent ( τ ). These findings provide evidence of the significant effect of fatigue induced by long exercise on the heart rate and speed variability.

Chaotic dynamics of cardioventilatory coupling in humans: effects of ventilatory modes

Cardioventilatory coupling (CVC), a transient temporal alignment between the heartbeat and inspiratory activity, has been studied in animals and humans mainly during anesthesia. The origin of the coupling remains uncertain, whether or not ventilation is a main determinant in the CVC process and whether the coupling exhibits chaotic behavior. In this frame, we studied sedative-free, mechanically ventilated patients experiencing rapid sequential changes in breathing control during ventilator weaning during a switch from a machine-controlled assistance mode [assist-controlled ventilation (ACV)] to a patient-driven mode [inspiratory pressure support (IPS) and unsupported spontaneous breathing (USB)]. Time series were computed as R to start inspiration (RI) and R to the start of expiration (RE). Chaos was characterized with the noise titration method (noise limit), largest Lyapunov exponent (LLE) and correlation dimension (CD). All the RI and RE time series exhibit chaotic behavior. Specific coupling patterns were displayed in each ventilatory mode, and these patterns exhibited different linear and chaotic dynamics. When switching from ACV to IPS, partial inspiratory loading decreases the noise limit value, the LLE, and the correlation dimension of the RI and RE time series in parallel, whereas decreasing intrathoracic pressure from IPS to USB has the opposite effect. Coupling with expiration exhibits higher complexity than coupling with inspiration during mechanical ventilation either during ACV or IPS, probably due to active expiration. Only 33% of the cardiac time series (RR interval) exhibit complexity either during ACV, IPS, or USB making the contribution of the cardiac signal to the chaotic feature of the coupling minimal. We conclude that 1) CVC in unsedated humans exhibits a complex dynamic that can be chaotic, and 2) ventilatory mode has major effects on the linear and chaotic features of the coupling. Taken together these findings reinforce the role of ventilation in the CVC process.

Large‐scale volume data analysis for meso‐ and macroscopic physiological modeling

Modeling is increasingly important to biological investigations, from basic cell biology to cognitive neuroscience. For many years, however, models and simulations have been severely limited by a lack of spatial, i.e. anatomical, realism. Simulation methods that incorporate highly detailed anatomy are expensive and complex, but developers are targeting ever increasing 3‐D spatial realism and have made major advances in recent years. Thus there is a renewed need for quantitative 3‐D anatomical data at subcellular to tissue scales, with many remaining challenges: (1) Automating detailed analyses from varied imaging modalities; (2) Handling potentially enormous datasets; (3) Correcting artifacts introduced by tissue preparation and imaging method; (4) Annotating datasets to include model parameters, e.g., molecular constituents and physical properties of intra‐ and extracellular spaces and membrane compartments; (5) Production of surface and volume meshes that include annotated properties; and (6) Formatting of meshes and properties for use with one or more simulation methods. We are developing PSC Volume Browser, DReAMM, and MCell software to address all of these challenges, with special emphasis on arbitrarily large volumetric datasets and Monte Carlo diffusion‐reaction simulation methods. We will present examples ranging from semi‐automated analysis of cardiac time series from micro‐CT imaging, to nonlinear registration and alignment of large‐scale serial EM sections, surface mesh generation using volume segmentation and computer‐aided design approaches, and quantitative simulation of microphysiological events such as calcium‐dependent neurotransmitter release from nerve terminal active zones. Supported by NIH P41 RR06009, R01 GM068630, NLM N01‐LM‐9‐3531, and NIH/NSF 0234002.

EVIDENCE CONSISTENT WITH DETERMINISTIC CHAOS IN HUMAN CARDIAC DATA: SURROGATE AND NONLINEAR DYNAMICAL MODELING

Whether or not the human cardiac system is chaotic has long been a subject of interest in the application of nonlinear time series analysis. The surrogate data method, which identifies an observed time series against three common kinds of hypotheses, does not provide sufficient evidence to confirm the existence of deterministic chaotic dynamics in cardiac time series, such as electrocardiogram data and pulse pressure propagation data. Moreover, these methods fail to exclude all but the most trivial hypothesis of linear noise. We present a recently suggested fourth algorithm for testing the hypotheses of a noise driven periodic orbit to decide whether these signals are consistent with deterministic chaos. Of course, we cannot exclude all other alternatives but our test is certainly stronger than the those applied previously. The algorithmic complexity is used as the discriminating statistic of the surrogate data method. We then perform nonlinear modeling for the short-term prediction between ECG and pulse data to provide further evidence that they conform to deterministic processes. We demonstrate the application of these methods to human electrocardiogram recordings and blood pressure propagation in the fingertip of seven healthy subjects. Our results indicate that bounded aperiodic determinism exists in both ECG and pulse time series. The addition of (the inevitable) dynamic noise means that it is not possible to conclude the underlying system is chaotic.

Nonstationarity and duration of the cardiac interval time series in assessing the functional state of operator personnel

The influence of nonstationarity in the time series of cardiac intervals on the assessment of the functional state (FS) of operator personnel was analyzed with a three-factor model of heart rhythm variability (HRV). ECG recordings were made in supine position at rest and in the sedentary position before and after important operator testing. In all three cases, the FS assessments were not influenced by nonstationarity of the input data. The effect of nonstationarity was also negligible for some particular HRV indices. Reliable assessments could be obtained from relatively short samples (256 down to 32 RR intervals) with prior norming of the factor indices for the corresponding segment length. The influence of the time series duration on the HRV indices was examined in various FSs; stable indices and proper recording conditions were determined.

Fractal rigidity by enhanced sympatho-vagal antagonism in heartbeat interval dynamics elicited by central application of corticotropin-releasing factor in mice

The dynamics of heartbeat interval fluctuations were studied in awake unrestrained mice following intracerebroventricular application of the neuropeptide corticotropin-releasing factor (CRF). The cardiac time series derived from telemetric ECG monitoring were analyzed by non-parametric techniques of nonlinear signal processing: delay-vector variance (DVV) analysis, higher-order variability (HOV) analysis, empirical mode decomposition (EMD), multiscale embedding-space decomposition (MESD), multiexponent multifractal (MEMF) analysis. The analyses support the conjecture that cardiac dynamics of normal control mice has both deterministic and stochastic elements, is nonstationary, nonlinear, and exerts multifractal properties. Central application of CRF results in bradycardia and increased variability of the beat-to-beat fluctuations. The altered dynamical properties elicited by CRF reflect a significant loss of intrinsic structural complexity of cardiac control which is due to central neuroautonomic hyperexcitation, i.e., enhanced sympatho-vagal antagonism. The change in dynamical complexity is characterized by an effect referred to as fractal rigidity, leading to a significant impairment of adaptability to extrinsic challenges in a fluctuating environment. The impact of dynamical neurocardiopathy as a major precipiting factor for the propensity of cardiac arrhythmias or sudden cardiac death by unchecked central CRF release in significant acute life events in man is critically discussed.

Heart rate variability in conscious neonatal swine: spectral features and responses to short-term intermittent hypoxia

BackgroundSpectral analysis of the cardiac time series has been used as a tool for assessing levels of parasympathetic and sympathetic modulation of the sinoatrial node. In the present investigation we evaluated daily changes in heart rate variability spectra in conscious neonatal piglets that were either neurally intact (n = 5) or had undergone right stellate ganglionectomy (n = 5). The partial stellectomized animals and their intact litter mates were exposed to four days of intermittent hypoxia, each day comprising nine episodes of hypoxia alternating with nine episodes of normoxia. A time control group (n = 7) comprised animals from different litters that were not exposed to intermittent hypoxia. We hypothesized that exposure to intermittent hypoxia would increase sympathetic efferent neuronal modulation of heart rate variability spectra in neurally intact animals and in those with right stellate ganglionectomy, and that his effect would be observed in heart rate variability spectra computed from baseline recordings.ResultsOverall, heart rate variability spectra during baseline conditions were dominated by high frequency activity, a reflection of parasympathetic efferent neuronal innervation and linkage to the ventilatory cycle manifested as respiratory sinus arrhythmia. Exposure to intermittent hypoxia did not alter daily baseline spectral features that would indicate an increase of sympathetic cardiac activity: low frequency (0.05 – 0.15 Hz) activity was unaffected and the ratio of low- to -high frequency activity remained less than unity indicating a predominance of high frequency activity. The resultant spectra were remarkably similar despite differences in cardiac sympathetic efferent neuronal innervation and experimental treatment. When spectra were computed from cardiac time series during representative hypoxic episodes, significant increases in activity across the low frequency region (0.05 – 0.15 Hz) of heart rate variability spectra were noted and were comparable in neurally intact animals and in those with right stellate ganglionectomy.ConclusionThe findings of this investigation provided important information regarding sympathetic efferent neuronal innervation of the heart during the neonatal period. Both neurally intact animals and those with right stellate ganglionectomy had equivalent increases of activity in the low frequency region of heart rate variability spectra during hypoxic stimulation. Such a finding demonstrated the capability of residual cardiac sympathetic neuronal innervation to affect functionally appropriate changes in cardiac chronotropy.

A New Potential Marker for Abnormal Cardiac Physiology in Depression

The purpose of the study was to determine if a nonlinear estimate of dimensional complexity, Pointwise correlation dimension (Pd2), could be used to identify abnormal cardiac physiology associated with depression in primary care outpatients. The subjects were 22 medical controls and 30 general medical outpatients with depression who wore a cardiac monitor for 24 h. There was a significant difference between depressed and control subjects for Pd2 based on the entire cardiac time-series, with depressed subjects exhibiting higher Pd2 values. A cutoff score reflecting high dimensional complexity was selected, and an odds ratio was calculated demonstrating that patients with Pd2 values above the cut-off were 8.8 times more likely to fall in the depressed group than the control group. Additional research is needed to determine if Pd2 is useful for identifying physiological markers of depression.

Self-Organizing Neural Network Analyses of Cardiac Data in Depression

Objective: To determine if an unsupervised self-organizing neural network could create a clinically meaningful distinction of ‘depression’ versus ‘no depression’ based on cardiac time-series data. Design: A self-organizing map (SOM) was used to separate the time-series of 84 subjects into groups based on characteristics of the data alone. Materials and Methods: Analyses included natural log transformations and two types of filtering to enhance characteristics of the data as well as classifications of unprocessed data. A Pearson χ² analysis was performed to determine if the SOM groups bore any relation to the binary clinical groups. Results: Overall correct SOM classifications ranged from 54 to 70.2% with two classifications being clinically meaningful. Conclusions: SOM classifications of cardiac time-series data with enhanced ultradian variations and cardiac data recorded around the interval when a person was in bed were useful in differentiating clinically meaningful subgroups with and without depression.

FRACTAL CHANGES IN HEART RATE DYNAMICS WITH AGING AND HEART FAILURE

In this work we analyze interbeat cardiac time series arising of three groups: healthy young and healthy elderly subjects and patients with congestive heart failure. We use the fractal dimension method proposed by Higuchi. We find that fractal organization is different for each group. In the case of healthy young subjects only one value of the fractal dimension is necessary to fit the interbeat data, whereas in the cases of healthy elderly and patients with congestive heart failure a crossover behavior in the fractal dimension is present but in opposite directions. By means of a "zoom" on the hinges of the crossover point interesting effects of aging are presented. Finally, we discuss our results in the context of heart interbeat dynamics.

Decreased chaos after exercise in cardiac output time series of rats: a preliminary report

1. Background: The post-exercise period is associated with a profound decrease in sympathetic activity and arterial pressure. We have recently shown that endurance exercise training in rats is associated with highly significant decreases of low frequency (LF: 0.19– 0.61 Hz ) power of blood pressure (BP). In this study, we measured cardiac output (CO) and systolic blood pressure (SBP) continuously for at least 5 min in rats before ( n=5) and after ( n=4) exercise. The purpose of this study was to determine if acute exercise produces post-exercise changes in the low frequency power (LF: 0.19– 0.61 Hz ) of CO as the decrease in this band of BP appears to be mediated by the sympathetic nervous system. 2. Methods: The data were sampled at 200 Hz and the HR time series were derived from the SBP signal. The signals were sampled at 16 Hz and the spectral powers were obtained in the following bands; total power (TP: 0– 2.5 Hz ), very low frequency power (VLF: 0– 0.19 Hz ), low frequency power (LF: 0.19– 0.61 Hz ) and high frequency power (HF: 1.2– 2.5 Hz ). We also obtained the transfer magnitude and coherence between HR and CO. Finally, largest Lyapunov exponent (LLE) was obtained as a measure of chaos for HR and CO before and after exercise. 3. Results: There was a significant decrease in LLE of CO ( p<0.05) after exercise. 4. Conclusions: These results suggest that the CO time series appear to be chaotic in nature and the decrease in LLE after exercise, especially in relation to HR appears to be due to a decrease in sympathetic activity. These new nonlinear techniques may prove to be of additional value to the linear measures such as spectral analysis.

Fractal dynamics of heart beat interval fluctuations in corticotropin-releasing factor receptor subtype 2 deficient mice.

Non-linear fractal analysis of cardiac interbeat time series was performed in corticotropin-releasing factor receptor subtype 2 (CRFR2) deficient mice. Heart rate dynamics in mice constitutes a self-similar, scale-invariant, random fractal process with persistent intrinsic long-range correlations and inverse power-law properties. We hypothesized that the sustained tachycardic response elicited by intraperitoneal (ip) injection of human/rat CRF (h/rCRF) is mediated by CRFR2. In wildtype control animals, heart rate was increased to about maximum levels (approximately 750 bpm) while in CRFR2-deficient animals baseline values were retained (approximately 580 bpm). The tachycardic response elicited by ip-application is mediated by CRFR2 and is interpreted to result from sympathetic stimulation. However, the functional integrity of CRFR2 would not present a prerequisite to maintaining the responsiveness and resiliency of cardiac control to external environmental perturbations experimentally induced by extrinsic ip-application of h/rCRF or under physiological conditions that may be associated with an increased peripheral release of CRF. Under stressful physiological conditions achieved by novelty exposure, CRFR2 is not involved in the cardiodynamic regulation to external short-term stress. While the hypothesis of involvement of CRFR2 in cardiac regulation upon pharmacological stimulation cannot be rejected, the present findings suggest that the mechanism of action is by sympathetic stimulation, but would not unambiguously allow to draw any conclusions as to the physiological role of CRFR2 in the control of cardiac dynamics.

Fractal fluctuations in cardiac time series

Human heart rate, controlled by complex feedback mechanisms, is a vital index of systematic circulation. However, it has been shown that beat-to-beat values of heart rate fluctuate continually over a wide range of time scales. Herein we use the relative dispersion, the ratio of the standard deviation to the mean, to show, by systematically aggregating the data, that the correlation in the beat-to-beat cardiac time series is a modulated inverse power law. This scaling property indicates the existence of long-time memory in the underlying cardiac control process and supports the conclusion that heart rate variability is a temporal fractal. We argue that the cardiac control system has allometric properties that enable it to respond to a dynamical environment through scaling.

A nonstationarity test for the spectral analysis of physiological time series with an application to respiratory sinus arrhythmia.

The spectral analysis of time series requires the signal to be at least weakly stationary; i.e., the mean, (co-) variance, and spectrum of the time series should not vary from segment to segment. It is commonly assumed that psychophysiological time series are not stationary. This study introduces a nonstationarity test to the psychophysiological literature, which is derived from evolutionary spectral analysis. Basically, the test consists of a double window technique in both the time and frequency domains, leading to a two-way analysis of variance for times and frequencies. In the current study, the nonstationarity test is applied to heart rate data obtained in a typical psychophysiological setting. Heart rate and respiration were measured in four age groups under four conditions--rest, paced breathing, vigilance, and reaction time. The results indicate that only few physiological time series were completely stationary. However, for every subject, and in every condition stationary stretches could be found that were long enough to apply spectral analysis. Spectral measures (power, coherence, and phase spectra) were then compared for stationary parts of the data and the total data. This comparison indicated that nonstationarity affects all spectral measures. Most importantly, Stationarity x Task Condition x Frequency Band interactions were observed for coherence and phase spectra, and there were significant interactions with age for each of the spectral indices. These findings suggest that nonstationarity may result in biased outcomes of significance tests of the effects of task manipulations on the spectral indices of cardiac time series. Thus, it was concluded that the stationarity test should be routinely applied in the spectral analysis of physiological time series. In addition, it was suggested that the nonstationarity test has an even wider range of application that might be of interest to the psychophysiologist.