Disentangling respiratory sinus arrhythmia in heart rate variability records

Topçu, Çağdaş; Frühwirth, Matthias; Moser, M.; Rosenblum, Michael G.; Pikovsky, Arkady

doi:10.1088/1361-6579/aabea4

Cited by 21 publications

(27 citation statements)

References 48 publications

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“…To overcome this limitation, we proposed filtering out the high-frequency oscillations from SAP and PI series and use the sequence method with both original and LP filtered series, so that both slow and fast baroreflex function can be estimated. Nevertheless, different approaches could be considered to exclude the influence of respiration on SAP and PI (Topçu et al, 2018). Results point that our proposal seems to be a reasonable alternative to the classical approach.…”

Section: Discussionmentioning

confidence: 99%

Revisiting the Sequence Method for Baroreflex Analysis

et al. 2019

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The sequence method is an important approach to assess the baroreflex function, mainly because it is based on the spontaneous fluctuations of beat-by-beat arterial pressure (for example, systolic arterial pressure or SAP) and pulse interval (PI). However, some studies revealed that the baroreflex effectiveness index (BEI), calculated through the sequence method, shows an intriguing oscillatory pattern as function of the delay between SAP and PI. It has been hypothesized that this pattern is related to the respiratory influence on SAP and/or PI variability, limiting the SAP ramps to 3 or 4 beats of length. In this study, this hypothesis was tested by assessing the sequence method using raw (original) and filtered series. Results were contrasted to the well-established transfer function, estimated between SAP and PI. Continuous arterial pressure recordings were obtained from healthy rats (N = 61) and beat-by-beat series of SAP and PI were generated. Low-pass (LP) and high-pass (HP) filtered series of SAP and PI were created by filtering the original series with a cutoff frequency of 0.8 Hz. Original series were analyzed by either the sequence method or cross-spectral analysis (transfer function) at low- (LF) and high- (HF) frequency bands, while filtered series were evaluated only by the sequence method. Baroreflex sensitivity (BRS) and BEI of original series, calculated by sequence method, was highly (85–90%) determined by HP series, with no significant association between original and LP series. A high correlation (>0.7) was found between the BRS estimated from original series (sequence method) and HF band (transfer function), as well as for LP series (sequence method) and LF band (transfer function). These findings confirmed the hypothesis that the sequence method quantifies only the high-frequency components of the baroreflex, neglecting the low-frequency influences, such as the Mayer waves. Therefore, we propose using both the original and LP filtered time series for a broader assessment of the baroreflex function using the sequence method.

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Section: Discussionmentioning

confidence: 99%

Revisiting the Sequence Method for Baroreflex Analysis

et al. 2019

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“…This "switching off" of a certain input is a particular case of a procedure for separating different forcing terms in the dynamics, called dynamical disentanglement. It has been recently applied to the analysis of interaction between cardiovascular and respiratory systems 24,34 and provided a separation of the respiratory-related and the nonrespiratory related heart rate variability with the help of the phase dynamics reconstruction. the evolution of an angular variable (protophase) is monotonic but not generally uniform, i.e.,θ = f (θ ).…”

Section: Discussionmentioning

confidence: 99%

Efficient determination of synchronization domains from observations of asynchronous dynamics

Rosenblum

Pikovsky

2018

Chaos: An Interdisciplinary Journal of Nonlinear Science

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We develop an approach for a fast experimental inference of synchronization properties of an oscillator. While the standard technique for determination of synchronization domains implies that the oscillator under study is forced with many different frequencies and amplitudes, our approach requires only several observations of a driven system. Reconstructing the phase dynamics from data, we successfully determine synchronization domains of noisy and chaotic oscillators. Our technique is especially important for experiments with living systems where an external action can be harmful and shall be minimized.

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“…In the subsequent study 31 , we extended this idea and generated artificial sequences of heartbeat events (R-peaks) according to the conditions ϕ (R) (t represents the heart rate variability due to internal fluctuations and external non-respiratory rhythms, e.g. blood pressure and blood perfusion rhythms.…”

Section: Disentanglement Of the Heart Rate Variabilitymentioning

confidence: 99%

Dynamical disentanglement in an analysis of oscillatory systems: an application to respiratory sinus arrhythmia

Rosenblum

Frühwirth²,

Moser

et al. 2019

Phil. Trans. R. Soc. A.

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We develop a technique for the multivariate data analysis of perturbed self-sustained oscillators. The approach is based on the reconstruction of the phase dynamics model from observations and on a subsequent exploration of this model. For the system, driven by several inputs, we suggest a dynamical disentanglement procedure, allowing us to reconstruct the variability of the system's output that is due to a particular observed input, or, alternatively, to reconstruct the variability which is caused by all the inputs except for the observed one. We focus on the application of the method to the vagal component of the heart rate variability caused by a respiratory influence. We develop an algorithm that extracts purely respiratory-related variability, using a respiratory trace and times of R-peaks in the electrocardiogram. The algorithm can be applied to other systems where the observed bivariate data can be represented as a point process and a slow continuous signal, e.g. for the analysis of neuronal spiking.

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Disentangling respiratory sinus arrhythmia in heart rate variability records

Abstract: The suggested technique can be exploited as a universal preprocessing tool, both for the analysis of respiratory influence on the heart rate and in cases when effects of other factors on the heart rate variability are in focus.

Cited by 21 publications

References 48 publications

Revisiting the Sequence Method for Baroreflex Analysis

Revisiting the Sequence Method for Baroreflex Analysis

Efficient determination of synchronization domains from observations of asynchronous dynamics

Dynamical disentanglement in an analysis of oscillatory systems: an application to respiratory sinus arrhythmia

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