Coordination in the Circulatory and Respiratory Systems

Raschke, F.

doi:10.1007/978-3-642-72637-8_18

Cited by 26 publications

(18 citation statements)

References 14 publications

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“…This in turn would result in a time delay between the onsets of one heart beat. This effect shows similarities to the trigger effect of single heart beats onto the beginning of the respiratory cycle described in [Ras87]. This hints at a connection between the cardiac centre and the respiratory generators in the brain stem.…”

Section: Discussionsupporting

confidence: 69%

“…As opposed to the synchronisation, which describes a phase-based relationship between systems, the coordination describes a time-based connection (e.g. between the time points of the onsets of respiratory cycles and the heart beats) and has been shown to play an important role for example in cardiorespiratory mechanisms [Ras86,Ras87,RH82].…”

Section: Synchronisation and Coordinationmentioning

confidence: 99%

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Coupling analysis of transient cardiovascular dynamics

Müller

Riedl

Penzel

et al. 2013

Biomedizinische Technik/Biomedical Engineering

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DissertationAmong the systems mentioned above, especially the cardiovascular system, whose complex behaviour is determined through many overlapping regulatory processes, is an ongoing researchtopic. An important section of this field comprises the interactions between the beat-to-beat intervals and the blood pressure, which on the one hand are at least known partially and on the other hand are controversially discussed.In this work, initially some existing coupling measures and their fields of application are introduced. One trait these measures have in common is the requirement of stationary time series to ensure their applicability. Therefore, in the course of this thesis a possibility to extend these measures is presented, which allows a coupling analysis with a high temporal resolution and thus also the analysis of transient, nonstationary events.The extension is based on the use of ensembles of time series and the calculation of the respective measures across these ensembles instead of across time. This allows for a temporal resolution of the same order of magnitude as the sampling rate in the original signal. The resolution only depends on the kind of coupling analysis method employed.The ensemble extension is applied to different coupling measures already successfully employed under difficult circumstances like high noise levels or short time series. For comparison, two simpler coupling measures are used. To begin with, the regarded tools are tested on various theoretical models and under different conditions. This is followed by a coupling analysis of cardiovascular time series recorded during transient events. The results on the one hand confirm topical study outcomes and on the other hand deliver new insights, which will allow to extend and improve cardiovascular system models in the future. Prediction methods based on these models will then be able to provide new diagnostic techniques and treatment procedures for cardiovascular diseases, thus contributing to health preservation in humans.

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Section: Discussionsupporting

confidence: 69%

Section: Synchronisation and Coordinationmentioning

confidence: 99%

Coupling analysis of transient cardiovascular dynamics

Müller

Riedl

Penzel

et al. 2013

Biomedizinische Technik/Biomedical Engineering

View full text Add to dashboard Cite

show abstract

“…In daytime during exercise, respiratory sinus arrhythmia is usually not visible, or is so weak that it cannot be depicted. During relaxation at rest and during sleep, in contrast, it is much larger and easily recognizable (Raschke, 1987). Early studies were even able to describe correlation between the extent of coupling and the various sleep stages.…”

Section: Respiration-related Changes Of Heart Rate and Ecgmentioning

confidence: 99%

Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography

et al. 2016

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The cardiac component of cardio-respiratory polysomnography is covered by ECG and heart rate recordings. However, their evaluation is often underrepresented in summarizing reports. As complements to EEG, EOG, and EMG, these signals provide diagnostic information for autonomic nervous activity during sleep. This review presents major methodological developments in sleep research regarding heart rate, ECG, and cardio-respiratory couplings in a chronological (historical) sequence. It presents physiological and pathophysiological insights related to sleep medicine obtained by new technical developments. Recorded nocturnal ECG facilitates conventional heart rate variability (HRV) analysis, studies of cyclical variations of heart rate, and analysis of ECG waveform. In healthy adults, the autonomous nervous system is regulated in totally different ways during wakefulness, slow-wave sleep, and REM sleep. Analysis of beat-to-beat heart-rate variations with statistical methods enables us to estimate sleep stages based on the differences in autonomic nervous system regulation. Furthermore, up to some degree, it is possible to track transitions from wakefulness to sleep by analysis of heart-rate variations. ECG and heart rate analysis allow assessment of selected sleep disorders as well. Sleep disordered breathing can be detected reliably by studying cyclical variation of heart rate combined with respiration-modulated changes in ECG morphology (amplitude of R wave and T wave).

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“…Human CRI has been studied for decades [23][24][25][30][31][32][33] . It has gained increased interest due to extensive application of ideas from nonlinear dynamics and information theory to medical problems, for example, to the use of heart rate variability (HRV) as an indicator of autonomic activity.…”

mentioning

confidence: 99%

In vivo cardiac phase response curve elucidates human respiratory heart rate variability

Frühwirth²,

et al. 2013

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Recovering interaction of endogenous rhythms from observations is challenging, especially if a mathematical model explaining the behaviour of the system is unknown. The decisive information for successful reconstruction of the dynamics is the sensitivity of an oscillator to external influences, which is quantified by its phase response curve. Here we present a technique that allows the extraction of the phase response curve from a non-invasive observation of a system consisting of two interacting oscillators-in this case heartbeat and respiration-in its natural environment and under free-running conditions. We use this method to obtain the phase-coupling functions describing cardiorespiratory interactions and the phase response curve of 17 healthy humans. We show for the first time the phase at which the cardiac beat is susceptible to respiratory drive and extract the respiratory-related component of heart rate variability. This non-invasive method for the determination of phase response curves of coupled oscillators may find application in many scientific disciplines.

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Coordination in the Circulatory and Respiratory Systems

Cited by 26 publications

References 14 publications

Coupling analysis of transient cardiovascular dynamics

Coupling analysis of transient cardiovascular dynamics

Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography

In vivo cardiac phase response curve elucidates human respiratory heart rate variability

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