Heartbeat synchronized with ventilation

Schäfer, Carsten; Rosenblum, Michael G.; Kurths, Jürgen; Abel, H

doi:10.1038/32567

Cited by 669 publications

(564 citation statements)

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“…The relationship between phase coupling of respiratory and cardiac rhythms and respiratory modulation of heart rate has been studied in spontaneous breathing at rest and sleep, and it has been demonstrated that phase synchronization and respiration-induced RRI oscillation represent different aspects of the cardiorespiratory interaction [4,32]. Our results show that the changes of phase and amplitude dynamics also exhibit different patterns during slow breathing exercise.…”

Section: Phase Coupling Versus Amplitude Oscillationmentioning

confidence: 60%

Effects of slow and regular breathing exercise on cardiopulmonary coupling and blood pressure

Zhang

Wang²,

Wu³

et al. 2016

Med Biol Eng Comput

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Meanwhile, BP was reduced significantly by the SPB procedure (SBP: from 122.0 ± 13.4 to 114.2 ± 14.9 mmHg, p < 0.001, DBP: from 82.2 ± 8.6 to 77.0 ± 9.8 mmHg, p < 0.001, PTT: from 172.8 ± 20.1 to 176.8 ± 19.2 ms, p < 0.001). Our results demonstrate that the SPB procedure can reduce BP and lengthen PTT significantly. Compared with amplitude dynamics, phase dynamics is a different marker for CPC analysis in reflecting cardiorespiratory coherence during slow breathing exercise. Our study provides a methodology to practice slow breathing exercise, including the setting of target breathing rate, change of CPC and the importance of regular breathing. The applications and usability of the study results have also been discussed.

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Section: Phase Coupling Versus Amplitude Oscillationmentioning

confidence: 60%

Effects of slow and regular breathing exercise on cardiopulmonary coupling and blood pressure

Zhang

Wang²,

Wu³

et al. 2016

Med Biol Eng Comput

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“…When a phase locking exists, the points in the bi-plot are not uniformly distributed and some structure is visible [12,13,14,15]. In case of linear phase coupling, the points in the bi-plot are ideally aligned along a straight line, in practice they cluster along a stripe whose slope indicates the frequency ratio (synchronization order) of the two signals.…”

Section: Instantaneous Frequency Analysismentioning

confidence: 99%

Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition

Balocchi

Menicucci

Santarcangelo

et al. 2004

Chaos, Solitons & Fractals

147

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Heart rate variability (HRV) is a well-known phenomenon whose characteristics are of great clinical relevance in pathophysiologic investigations. In particular, respiration is a powerful modulator of HRV contributing to the oscillations at highest frequency. Like almost all natural phenomena, HRV is the result of many nonlinearly interacting processes; therefore any linear analysis has the potential risk of underestimating, or even missing, a great amount of information content. Recently the technique of Empirical Mode Decomposition (EMD) has been proposed as a new tool for the analysis of nonlinear and nonstationary data. We applied EMD analysis to decompose the heartbeat intervals series, derived from one electrocardiographic (ECG) signal of 13 subjects, into their components in order to identify the modes associated with breathing. After each decomposition the mode showing the highest frequency and the corresponding respiratory signal were Hilbert transformed and the instantaneous phases extracted were then compared. The results obtained indicate a synchronization of order 1:1 between the two series proving the existence of phase and frequency coupling between the component associated with breathing and the respiratory signal itself in all subjects.

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“…This regulation occurs via a mechanical effect of breathing by synchronizing all cardiovascular variables at the respiratory rhythm (cf. also [Schafer et al, 1998]), particularly at a slow rate coincident with the Mayer waves in arterial pressure (approximately 6 cycles/min). The authors could show that slow breathing increases baroreflex sensitivity and reduces chemoreflex sensitivity, leading to increased parasympathetic and reduced sympathetic activity.…”

Section: Further Applicationsmentioning

confidence: 99%

Nonlinear Methods of Cardiovascular Physics and Their Clinical Applicability

Wessel

Malberg²,

Bauernschmitt

et al. 2007

Int. J. Bifurcation Chaos

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102

107

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In this tutorial we present recently developed nonlinear methods of cardiovascular physics and show their potentials to clinically relevant problems in cardiology. The first part describes methods of cardiovascular physics, especially data analysis and modeling of noninvasively measured biosignals, with the aim to improve clinical diagnostics and to improve the understanding of cardiovascular regulation. Applications of nonlinear data analysis and modeling tools are various and outlined in the second part of this tutorial: monitoring-, diagnosis-, course and mortality prognoses as well as early detection of heart diseases. We show, that these data analyses and modeling methods lead to significant improvements in different medical fields.Keywords: Cardiovascular physics; nonlinear data analysis and modeling; heart rate and blood pressure variability; baroreflex sensitivity. List of Abbreviations

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Heartbeat synchronized with ventilation

Cited by 669 publications

References 1 publication

Effects of slow and regular breathing exercise on cardiopulmonary coupling and blood pressure

Effects of slow and regular breathing exercise on cardiopulmonary coupling and blood pressure

Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition

Nonlinear Methods of Cardiovascular Physics and Their Clinical Applicability

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