Human Heartbeat Interval Research Articles

Jump events in the human heartbeat interval fluctuations

Jumps are discontinuous variations in time series and one expects that the higher jump activity will cause higher uncertainty in the stochastic behavior of measured time series. Here we study jump events in beat-to-beat fluctuations in the heart rates of healthy subjects, as well as those with congestive heart failure (CHF). The analysis shows that the interbeat time series belong to the class of non-continuous stochastic processes. The estimated drift and diffusion coefficients and jump characteristics of healthy and CHF subjects reveal the distinguishability of two subjects.

Filter-based multiscale entropy analysis of complex physiological time series

Multiscale entropy (MSE) has been widely and successfully used in analyzing the complexity of physiological time series. We reinterpret the averaging process in MSE as filtering a time series by a filter of a piecewise constant type. From this viewpoint, we introduce filter-based multiscale entropy (FME), which filters a time series to generate multiple frequency components, and then we compute the blockwise entropy of the resulting components. By choosing filters adapted to the feature of a given time series, FME is able to better capture its multiscale information and to provide more flexibility for studying its complexity. Motivated by the heart rate turbulence theory, which suggests that the human heartbeat interval time series can be described in piecewise linear patterns, we propose piecewise linear filter multiscale entropy (PLFME) for the complexity analysis of the time series. Numerical results from PLFME are more robust to data of various lengths than those from MSE. The numerical performance of the adaptive piecewise constant filter multiscale entropy without prior information is comparable to that of PLFME, whose design takes prior information into account.

Combination of equiprobable symbolization and time reversal asymmetry for heartbeat interval series analysis

Symbolic dynamics method and time reversal asymmetry analysis are both important approaches in the study of heartbeat interval series. However, there is limited research work reported on combining these two methods. We provide a method of time reversal asymmetry analysis which focuses on the differences between the forward and backward embedding "m words" after the operation of equiprobable symbolization. To investigate the total amplitude as well as the distribution features of the difference, four indices are proposed. Based on the application to simulation series, we found that these measures can successfully detect time reversal asymmetry in chaos series. With application to human heartbeat interval series (RR series), it is suggested that the distribution features of the forward-backward difference can sensitively capture the dynamical changes caused by diseases or aging. In particular, the index E(D), which reflects the random degree of the forward-backward difference distribution, can significantly discriminate healthy subjects from diseased ones. We conclude that RR series from healthy subjects show more asymmetry in temporal structure on the original time scale from the perspective of equiprobable symbolization, whereas diseases account for loss of this asymmetry.

The timing and temporal patterns of eye blinking are dynamically modulated by attention

A number of human behaviors and movements show self-similar temporal patterns in their occurrence over time. Human walking, finger tapping and heartbeat intervals have fluctuations that are statistically similar at multiple time scales. However, whether eye blinking, which is a unique human behavior that occurs spontaneously, embeds a similar temporal structure within other types of movements is largely unknown. In this study, we used attention-requiring tasks to assess how the temporal pattern of eye blinking is altered in both the second and sub-second time scales. Our results showed that eyeblink activity was more suppressed as the task difficulty level increased and was facilitated immediately after exposure to auditory stimuli, which were presented for 6 to 14s. Moreover, similar transient suppressive and facilitative patterns were observed in the response period, which lasted for less than one second. Furthermore, we found that spontaneous eye blinking intervals fluctuated according to an 1/f scaling property, which is widely observed in various human movements. These results suggest that the dynamics of eye blinking under specific cognitive tasks exhibit a similar temporal structure at multiple time scales.

Seven-hour multiunit recordings from rats reveal very long-term correlation in the cortical activity

In the present study, we investigate how history of activity is retained in neurons embedded in the intact brain. For this purpose, we employ the so-called multiscale analysis for the fluctuations of interspike intervals (ISIs). An ability of neuronal networks, but not of isolated neurons, to retain information at different time scales greatly enriches their computational ability because now they can access the information across the full space-time domain, rather than spatially but at a single temporal scale. We analyze the intact brain of rats without anesthesia using a special chamber[1], namely the neural activity of the normally working brain. Recorded data with multiunit electrodes reveal evidence for the long-term in neuronal activity. We apply multiscale analysis because it has been proven to be powerful to uncover multiple time scales in hydrodynamic turbulence [2], human heartbeat interval fluctuations [3,4], stock price fluctuations [5], etc. In the multiscale analysis, we study concatenated ISIs instead of the original ISIs. We first determine a ‘scale’ that specifies how many consecutive ISI are concatenated (connected) and ask how large the fluctuations of the concatenated ISIs are. The upper panel of Figure 1 shows the fluctuations at scale of four (s=4) and the lower panel shows the corresponding histogram displayed in the semilog coordinate. Gaussian fluctuations should be shaped as a parabola in this coordinate. The histogram in Figure 2 implies that the fluctuations are highly non-Gaussian. As we increase the scale, more and more ISIs are concatenated. The central limiting theorem should ensure that the histogram become increasingly Gaussian if ISIs are statistically independent. However, Figure 3 shows that at scale s=32 or at even s=256, the histogram remains non-Gaussian. This implies very strong non-Gaussianity or strong long-term correlation of ISIs.

Investigating fractal property and respiratory modulation of human heartbeat time series using empirical mode decomposition

The human heartbeat interval reflects a complicated composition with different underlying modulations and the reactions against environmental inputs. As a result, the human heartbeat interval is a complex time series and its complexity can be scaled using various physical quantifications, such as the property of long-term correlation in detrended fluctuation analysis (DFA). Recently, empirical mode decomposition (EMD) has been shown to be a dyadic filter bank resembling those involved in wavelet decomposition. Moreover, the hierarchy of the extracted modes may be exploited for getting access to the Hurst exponent, which also reflects the property of long-term correlation for a stochastic time series. In this paper, we present significant findings for the dynamic properties of human heartbeat time series by EMD. According to our results, EMD provides a more accurate access to long-term correlation than Hurst exponent does. Moreover, the first intrinsic mode function (IMF 1) is an indicator of orderliness, which reflects the modulation of respiratory sinus arrhythmia (RSA) for healthy subjects or performs a characteristic component similar to that decomposed from a stochastic time series for subjects with congestive heart failure (CHF) and atrial fibrillation (AF). In addition, the averaged amplitude of IMF 1 acts as a parameter of RSA modulation, which reflects significantly negative correlation with aging. These findings lead us to a better understanding of the cardiac system.

Characterizing Nonlinear Heartbeat Dynamics Within a Point Process Framework

Human heartbeat intervals are known to have nonlinear and nonstationary dynamics. In this paper, we propose a model of R-R interval dynamics based on a nonlinear Volterra-Wiener expansion within a point process framework. Inclusion of second-order nonlinearities into the heartbeat model allows us to estimate instantaneous heart rate (HR) and heart rate variability (HRV) indexes, as well as the dynamic bispectrum characterizing higher order statistics of the nonstationary non-gaussian time series. The proposed point process probability heartbeat interval model was tested with synthetic simulations and two experimental heartbeat interval datasets. Results show that our model is useful in characterizing and tracking the inherent nonlinearity of heartbeat dynamics. As a feature, the fine temporal resolution allows us to compute instantaneous nonlinearity indexes, thus sidestepping the uneven spacing problem. In comparison to other nonlinear modeling approaches, the point process probability model is useful in revealing nonlinear heartbeat dynamics at a fine timescale and with only short duration recordings.

Intrinsic Mode Analysis of Human Heartbeat Time Series

The human heartbeat interval is determined by complex nerve control and environmental inputs. As a result, the heartbeat interval for a human is a complex time series, as shown by previous studies. Most of the analysis algorithms proposed for characterizing the profile of heartbeat time series, such as detrended fluctuation analysis and multi-scale entropy, are based on various characteristics of dynamics. In this study, we present an empirical mode decomposition-based intrinsic mode analysis, which uses the appearance energy index (AEI) to quantify the property of long-term correlation, and structure index (SI) to characterize the internal modulation of data. This presented algorithm was used to investigate the human heartbeat time series downloaded from PhysioBank. We found the profiles of human heartbeat time series of subjects with congestive heart failure (CHF) or atrial fibrillation (AF) are significantly different from those of healthy subjects in internal modulation as shown by SI. Moreover, AEI is the critical characteristics for verifying subjects with CHF from subjects with AF in a degree of long-term correlation. Both AEI and SI contribute to presenting the characteristic profiles of a human heartbeat time series.

Human heart beat analysis using a modified algorithm of detrended fluctuation analysis based on empirical mode decomposition

How to quantify the complexity of a physiological signal is a crucial issue for verifying the underlying mechanism of a physiological system. The original algorithm of detrended fluctuation analysis (DFA) quantifies the complexity of signals using the DFA scaling exponent. However, the DFA scaling exponent is suitable only for an integrated time series but not the original signal. Moreover, the method of least squares line is a simple detrending operation. Thus, the analysis results of the original DFA are not sufficient to verify the underlying mechanism of physiological signals. In this study, we apply an innovative timescale-adaptive algorithm of empirical mode decomposition (EMD) as the detrending operation for the modified DFA algorithm. We also propose a two-parameter scale of randomness for DFA to replace the DFA scaling exponent. Finally, we apply this modified algorithm to the database of human heartbeat interval from Physiobank, and it performs well in identifying characteristics of heartbeat interval caused by the effects of aging and of illness.

A MODIFIED CHUA CIRCUIT SIMULATES 1/f-LIKE FLUCTUATION OF HEARTBEAT INTERVALS

The low-frequency portion of the power spectrum of human heartbeat intervals exhibits a 1/f-like scaling behavior. Despite its clinical significance, the mechanism remains unknown. By recording heartbeat intervals, renal sympathetic nerve activity (SNA), and blood pressure (BP) in conscious rats with normal or high BP, we reported that the scaling slope of heartbeat intervals is steeper in the rats with high BP, and that SNA leads to heartbeat interval and BP changes, and also the dynamics of these three variables, heartbeat intervals, BP, and SNA, results from a low-dimensional chaos. This hemodynamics is modeled excellently by modification of a known chaotic electrical circuit, Chua circuit. Increasing the resistive element between SNA and BP in the circuit increases the scaling slope and abolishes the low-dimensional chaos. Therefore, sensitivity of BP to sympathetic control is likely to determine the scaling slopes of heartbeat intervals in health and disease.

A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability.

Heart rate is a vital sign, whereas heart rate variability is an important quantitative measure of cardiovascular regulation by the autonomic nervous system. Although the design of algorithms to compute heart rate and assess heart rate variability is an active area of research, none of the approaches considers the natural point-process structure of human heartbeats, and none gives instantaneous estimates of heart rate variability. We model the stochastic structure of heartbeat intervals as a history-dependent inverse Gaussian process and derive from it an explicit probability density that gives new definitions of heart rate and heart rate variability: instantaneous R-R interval and heart rate standard deviations. We estimate the time-varying parameters of the inverse Gaussian model by local maximum likelihood and assess model goodness-of-fit by Kolmogorov-Smirnov tests based on the time-rescaling theorem. We illustrate our new definitions in an analysis of human heartbeat intervals from 10 healthy subjects undergoing a tilt-table experiment. Although several studies have identified deterministic, nonlinear dynamical features in human heartbeat intervals, our analysis shows that a highly accurate description of these series at rest and in extreme physiological conditions may be given by an elementary, physiologically based, stochastic model.

Asymmetrical singularities in real-world signals

We generalize the wavelet transform modulus maxima approach in order to analyze positive and negative changes separately and show different singularity spectra depending on the direction of changes in (i) human heartbeat interval data during sympathetic blockade, (ii) time series of daytime human physical activity of healthy individuals (but not of patients with debilitating fatigue), and (iii) daily stock price records of the Nikkei 225 in the period 1990-2002--but not of the S&P 500. We conclude that the analysis of asymmetrical singularities provides deeper insights into the underlying complexity of real-world signals that can greatly enhance our understanding of the mechanisms determining the systems' dynamics.

Self-affine fractal variability of human heartbeat interval dynamics in health and disease.

The complexity of the cardiac rhythm is demonstrated to exhibit self-affine multifractal variability. The dynamics of heartbeat interval time series was analyzed by application of the multifractal formalism based on the Cramèr theory of large deviations. The continuous multifractal large deviation spectrum uncovers the nonlinear fractal properties in the dynamics of heart rate and presents a useful diagnostic framework for discrimination and classification of patients with cardiac disease, e.g., congestive heart failure. The characteristic multifractal pattern in heart transplant recipients or chronic heart disease highlights the importance of neuroautonomic control mechanisms regulating the fractal dynamics of the cardiac rhythm.

Scale invariance in the nonstationarity of human heart rate.

We introduce a segmentation algorithm to probe the temporal organization of heterogeneities in human heartbeat interval time series. We find that the lengths of segments with different local mean heart rates follow a power-law distribution and show that this scale-invariant structure is not a simple consequence of the long-range correlations present in the data. The differences in mean heart rates between consecutive segments display a common functional form, but with different parameters for healthy individuals and for heart-failure patients. These findings suggest that there is relevant physiological information hidden in the heterogeneities of the heartbeat time series.

Operation Everest II: an indication of deterministic chaos in human heart rate variability at simulated extreme altitude.

It has been shown that fluctuation of human heartbeat intervals (heart rate variability, HRV) reflects variations in autonomic nervous system activity. We studied HRV at simulated altitudes of over 6000 m from Holter electrocardiograms recorded during the Operation Everest II study (Houston et al. 1987). Stationary, approximately 30-min segments of HRV data from six subjects at sea level and over 6000 m were supplied to (1) spectral analysis to evaluate sympathetic and parasympathetic nervous system (SNS, PNS) activity, (2) the analysis of Poincaré section of the phase space trajectory reconstructed on a delayed coordinate system to evaluate whether there was fluctuation with deterministic dynamics, (3) the estimation of the correlation dimension to evaluate a static property of putative attractors, and (4) the analysis of nonlinear predictability of HRV time series which could reflect a dynamic property of the attractor. Unlike HRV at sea level, the recordings at over 6000 m showed a strong periodicity (period of about 20 s) with small cycle-to-cycle perturbation. When this perturbation was expressed on a Poincaré section, it seemed to be likely that the perturbation itself obeyed a deterministic law. The correlation dimensions of these recordings showed low dimensional values (3.5 +/- 0.4, mean +/- SD), whereas those of the isospectral surrogates showed significantly (P < 0.05) higher values (5.3 +/- 0.5) with embedding dimensions of 5.6 +/- 0.9.(ABSTRACT TRUNCATED AT 250 WORDS)