Dynamic heart rate variability: a tool for exploring  sympathovagal balance continuously during sleep in men

Otzenberger, Hélène; Gronfier, Claude; Simon, Chantal; Charloux, Anne; Ehrhart, J; Piquard, François; Brandenberger, G.

doi:10.1152/ajpheart.1998.275.3.h946

Cited by 170 publications

(175 citation statements)

References 25 publications

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“…60 ± 5 beats per minute with an average standard deviation of approximately 6%. As found in other physiological studies involving heart rate variability (Akselrod et al, 1981;Cohen and Taylor, 2002;Otzenberger et al, 1998), the spectra of measured cardiac rates variations (Figure 2) was greatest at the low frequencies (< 0.1 Hz) probed in this study, but with no distinct frequency peaks. This low frequency region contains both the BOLD fMRI signal fluctuations associated with functional connectivity and the stimulus frequencies for most block-design fMRI paradigms.…”

Section: Discussionsupporting

confidence: 87%

“…Across-beat fluctuations in the heart rate have been found to occur in several frequency bands (Akselrod et al, 1981;Cohen and Taylor, 2002;Otzenberger et al, 1998) including the low frequency region investigated in fMRI studies of resting-state functional connectivity (< 0.1 Hz). In fact, a recent study suggests that systemic cardiovascular fluctuations (low frequency oscillations in the heart rate and arterial blood pressure) can account for about half of the information carried with low frequency oscillations in the cerebral haemodynamics (specifically in the oxyhaemoglobin concentration change) (Katura et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal

et al. 2007

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Heart rate fluctuations occur in the low frequency region (< 0.1 Hz) probed in functional magnetic resonance imaging (fMRI) studies of resting-state functional connectivity and most fMRI block paradigms, and may be related to low frequency blood-oxygenation-level-dependent (BOLD) signal fluctuations. To investigate this hypothesis, temporal correlations between cardiac rate and restingstate fMRI signal timecourses were assessed at 3 Tesla. Resting-state BOLD fMRI and accompanying physiological data were acquired and analyzed using cross-correlation and regression. Time-shifted cardiac rate timecourses were included as regressors in addition to established physiological regressors (RETROICOR (Glover et al., 2000) and respiration volume per unit time (Birn et al., 2006b)). Significant correlations between the cardiac rate and BOLD signal timecourses were revealed, particularly negative correlations in gray matter at time-shifts of 6-12 seconds and positive correlations at time shifts of 30-42 seconds (TR = 6 s). Regressors consisting of cardiac rate timecourses shifted by delays of between 0 and 24 seconds explained an additional 1 % of the BOLD signal variance on average over the whole brain across 9 subjects, a similar additional variance to that explained by respiration volume per unit time and RETROICOR regressors, even when used in combination with these other physiological regressors. This suggests that including such time-shifted cardiac rate regressors will be beneficial for explaining physiological noise variance and will thereby improve the statistical power in future task-based and resting-state fMRI studies.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal

et al. 2007

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“…Equation (9) states that the sum of the short-term and the long-term variability is the total variability. Equation (10) affirms that the long-term variability is the total variability minus the contribution due to short-term variability.…”

Section: Short-and Long-term Variabilitymentioning

confidence: 75%

“…Some researchers have employed the correlation coefficient of the Poincaré plot to characterize its shape [10]. This measure is:…”

Section: Correlation Coefficientmentioning

confidence: 99%

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New insights into the relationship between Poincare plot geometry and linear measures of heart rate variability

Brennan

Palaniswami

Kamen³

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-The Poincaré plot is an emerging Heart Rate Variability (HRV) analysis technique, the geometry of which has been shown to distinguish between healthy and unhealthy subjects in clinical settings. The Poincaré plot is able to display nonlinear aspects of the interval sequence and is therefore of interest in characterizing the nonlinear aspects of HRV. The problem is, how do we quantitatively characterize the geometry of the plot to capture useful descriptors that are independent of existing HRV measures? In this paper, we investigate a popular existing category of techniques and show that they measure linear aspects of the intervals which existing HRV indices already specify. The fact that these methods appear insensitive to the nonlinear characteristics of the intervals is an important finding because the Poincaré plot is primarily a nonlinear technique.Keywords: HRV, Poincaré plot, nonlinear analysis I. INTRODUCTIONThe field of heart rate variability (HRV) studies the fluctuations in the intervals between heartbeats, known as RR intervals. The Poincaré plot, a technique taken from nonlinear dynamics, portrays the nature of these fluctuations graphically. It is a scatter-plot of each RR interval plotted against the next interval. Poincaré plot analysis is an emerging quantitative-visual technique whereby the shape of the plot is categorized into functional classes that indicate the degree of heart failure in a subject [1,2]. The plot provides summary information as well as detailed beat-to-beat information on the behavior of the heart [3].Support is increasing for nonlinear analysis techniques and quantitative descriptors as it has become evident that the cardiac systems are nonlinear in their function [4]. The Poincaré plot is becoming a popular technique due to its simple visual interpretation and its proven clinical ability as a predictor of disease and cardiac dysfunction [5]. The problem regarding Poincaré plot use has been the lack of obvious quantitative measures that characterize the salient features of the plot.Researchers have put forward a number of techniques that attempt to quantitatively summarize the plot's geometric appearance. The efforts can be summarized into 3 categories: geometrical descriptors, scanning parameters and image distribution parameters [6]. Of these, the geometrical descriptors are the most popular in the clinical and physiological HRV literature.In this study, we consider the geometrical Poincaré plot descriptors. We provide expressions that connect each descriptor to existing linear measures of HRV. This accomplishes two things. Firstly, it provides insight into Poincaré plot geometry in terms of the well-understood existing indices of HRV. Secondly, it shows that these measures are not independent to the existing standard linear statistics. Therefore, the intrinsic ability of the Poincaré plot to identify nonlinear beat-to-beat structure is not being exploited by these techniques. II. THE LINEAR HRV INDICES A. Standard deviation of the RR intervalsThe standard deviat...

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Exposure to strong ELF magnetic fields does not alter cardiac autonomic control mechanisms

Graham

Sastre

Cook

et al. 2000

Bioelectromagnetics

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Clinical and epidemiological studies attest that alterations in heart rate variability (HRV) are predictive of specific types of cardiovascular morbidity and mortality in otherwise healthy persons. Recent reports also suggest that changes in HRV may be associated with exposure to intermittent magnetic fields (60 Hz, 28.3 microT) in the laboratory and that mortality is increased in cardiac disease categories related to altered HRV for utility workers whose jobs involve longer exposure to elevated magnetic fields. This study combined three approaches to learn more about the specific exposure circumstances under which changes in HRV occur. First, cardiac autonomic control, as indexed by HRV spectral analysis measures, was measured in 24 men during exposure to a much higher intensity field than any previously examined (resultant flux density = 127.3 microT [1273 milliGauss, mG]). Second, HRV measures from the same individual were compared across three relevant test conditions: intermittent and continuous field exposure and during a no-exposure, control condition. Third, electrocardiographic data were analyzed to determine if the precise timing of when the magnetic field switched on or off in relation to the cardiac cycle results in phase-resetting of the human cardiac rhythm. HRV measures were not altered by either field exposure condition compared to the control condition, and no evidence for a phase-resetting mechanism was found. Further research is needed to resolve the differences between the present and the earlier laboratory-based studies of HRV and to determine if cardiac rhythm disturbances are associated with exposure to the more complex magnetic fields found in the man-made environment.

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Dynamic heart rate variability: a tool for exploring sympathovagal balance continuously during sleep in men

Cited by 170 publications

References 25 publications

Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal

Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal

New insights into the relationship between Poincare plot geometry and linear measures of heart rate variability

Exposure to strong ELF magnetic fields does not alter cardiac autonomic control mechanisms

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