Causality Between the Amplitude and Frequency of Cardiac Oscillations

Paluš, Milan; Stefanovska, Aneta; Veber, Mitja

doi:10.1023/b:care.0000031540.47076.d1

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…Information domain approaches have been already proposed for the assessment of causality in CV interactions (Nollo et al, 2002 ; Palus et al, 2004 ; Porta et al, 2011b ). In particular, Porta et al ( 2011b ) have recently shown that cross-CE indexes perform better than more traditional approaches relying on the transformation of frequency domain phase shifts into time delays (Taylor and Eckberg, 1996 ; Pitzalis et al, 1998 ; Cooke et al, 1999 ).…”

Section: Discussionmentioning

confidence: 99%

“…Notwithstanding this, non-linear approaches are often desired as they may account better than linear methods for the high number and complexity of the mechanisms underlying CV and cardiorespiratory variability. The evaluation of non-linear causal coupling between cardiac, vascular, and respiratory time series has been performed in the past according to approaches based on phase synchronization (Rosenblum et al, 2002 ), non-linear prediction (Faes and Nollo, 2006 ; Faes et al, 2008b ), non-linear model identification (Faes et al, 2008a ; Riedl et al, 2010 ), symbolic coupling traces (Wessel et al, 2009 ; Suhrbier et al, 2010 ), and information theory (Porta et al, 1999 ; Palus et al, 2004 ). Among others, methods based on information theory constitute a valid, model-free approach to quantify non-linear causality based on the information amount transferred from one series to the other.…”

Section: Introductionmentioning

confidence: 99%

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Information Domain Approach to the Investigation of Cardio-Vascular, Cardio-Pulmonary, and Vasculo-Pulmonary Causal Couplings

Faes¹,

Nollo²,

Porta³

2011

Front. Physio.

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The physiological mechanisms related to cardio-vascular (CV), cardio-pulmonary (CP), and vasculo-pulmonary (VP) regulation may be probed through multivariate time series analysis tools. This study applied an information domain approach for the evaluation of non-linear causality to the beat-to-beat variability series of heart period (t), systolic arterial pressure (s), and respiration (r) measured during tilt testing and paced breathing (PB) protocols. The approach quantifies the causal coupling from the series i to the series j (Cij) as the amount of information flowing from i to j. A measure of directionality is also obtained as the difference between two reciprocal causal couplings (Di,j = Cij − Cji). Significant causal coupling and directionality were detected respectively when the median of Cij over subjects was positive (Cij > 0), and when Di,j was statistically different from zero (Di,j > 0 or Di,j < 0). The method was applied on t, s, and r series measured in 15 healthy subjects (22–32 years, 8 males) in the supine (su) and upright (up) positions, and in further 15 subjects (21–29 years, 7 males) during spontaneous (sp) and paced (pa) breathing. In the control condition (su, sp), a significant causal coupling was observed for Crs, Crt, Cst, and Cts, and significant directionality was present only from r to t (Dr,t > 0). During head-up tilt (up, sp), Crs was preserved, Crt decreased to zero median, and Cst and Cts increased significantly; directionality vanished between r and t (Dr,t = 0) and raised from s to t (Ds,t > 0). During PB (su, pa), Crs increased significantly, Crt and Cts were preserved, and Cst decreased to zero median; directionality was preserved from r to t (Dr,t > 0), and raised from r to s (Dr,s > 0). These results suggest that the approach may reflect modifications of CV, CP, and VP mechanisms consequent to altered physiological conditions, such as the baroreflex engagement and the dampening of respiratory sinus arrhythmia induced by tilt, or the respiratory driving on arterial pressure induced by PB. Thus, it could be suggested as a tool for the non-invasive monitoring of CV and cardiorespiratory control systems in normal and impaired conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Information Domain Approach to the Investigation of Cardio-Vascular, Cardio-Pulmonary, and Vasculo-Pulmonary Causal Couplings

Faes¹,

Nollo²,

Porta³

2011

Front. Physio.

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“…After integration, the maxima and minima of the resultant signal represent the systolic and diastolic pressures. They were shown to be at positions identical to those obtained with an infrared sensor (Paluš et al 2004). The arrival time of the pressure pulse was determined from the minimum of the curve at the end of Marker points were detected and the pulse arrival times were calculated from the R-peak.…”

Section: Experimental Protocolmentioning

confidence: 93%

Pulse transit times to the capillary bed evaluated by laser Doppler flowmetry

Bernjak

Stefanovska

2009

Physiol. Meas.

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The pulse transit time (PTT) of a wave over a specified distance along a blood vessel provides a simple non-invasive index that can be used for the evaluation of arterial distensibility. Current methods of measuring the PTT determine the propagation times of pulses only in the larger arteries. We have evaluated the pulse arrival time (PAT) to the capillary bed, through the microcirculation, and have investigated its relationship to the arterial PAT to a fingertip. To do so, we detected cardiac-induced pulse waves in skin microcirculation using laser Doppler flowmetry (LDF). Using the ECG as a reference, PATs to the microcirculation were measured on the four extremities of 108 healthy subjects. Simultaneously, PATs to the radial artery of the left index finger were obtained from blood pressure recordings using a piezoelectric sensor. Both PATs correlate in similar ways with heart rate and age. That to the microcirculation is shown to be sensitive to local changes in skin perfusion induced by cooling. We introduce a measure for the PTT through the microcirculation. We conclude that a combination of LDF and pressure measurements enables simultaneous characterization of the states of the macro and microvasculature. Information about the microcirculation, including an assessment of endothelial function, may be obtained from the responses to perturbations in skin perfusion, such as temperature stress or vasoactive substances.

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“…The second measure, the conditional mutual information (Paluš 1996), bears some similarities with the transfer entropy and is in some cases equivalent. It has been widely applied to neurophysiological and cardiovascular data (Paluš et al 2001a, Paluš et al 2004, Paluš and Stefanovska 2003, Frenzel and Pompe 2007, Musizza et al 2007, Paluš 2007, Vejmelka 2008, Faes et al 2011, Quinn et al 2011, Sun and Bollt 2014. This approach can also be used on phase time series.…”

Section: Entropy-basedmentioning

confidence: 99%

Causality in physiological signals

et al. 2016

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Health is one of the most important non-material assets and thus also has an enormous influence on material values, since treating and preventing diseases is expensive. The number one cause of death worldwide today originates in cardiovascular diseases. For these reasons the aim of understanding the functions and the interactions of the cardiovascular system is and has been a major research topic throughout various disciplines for more than a hundred years. The purpose of most of today's research is to get as much information as possible with the lowest possible effort and the least discomfort for the subject or patient, e.g. via non-invasive measurements. A family of tools whose importance has been growing during the last years is known under the headline of coupling measures. The rationale for this kind of analysis is to identify the structure of interactions in a system of multiple components. Important information lies for example in the coupling direction, the coupling strength, and occurring time lags. In this work, we will, after a brief general introduction covering the development of cardiovascular time series analysis, introduce, explain and review some of the most important coupling measures and classify them according to their origin and capabilities in the light of physiological analyses. We will begin with classical correlation measures, go via Granger-causality-based tools, entropy-based techniques (e.g. momentary information transfer), nonlinear prediction measures (e.g. mutual prediction) to symbolic dynamics (e.g. symbolic coupling traces). All these methods have contributed important insights into physiological interactions like cardiorespiratory coupling, neuro-cardio-coupling and many more. Furthermore, we will cover tools to detect and analyze synchronization and coordination (e.g. synchrogram and coordigram). As a last point we will address time dependent couplings as identified using a recent approach employing ensembles of time series. The scope of this review, as opposed to various other excellent reviews like (Hlaváčková-Schindler et al Phys. Rep. 441 1-46, Kramer et al 2004 Phys. Rev. E 70 1-10, Lombardi 2000 Circulation 101 8-10, Porta et al 2000 Am. J. Physiol.: Heart and Circulatory Physiol. 279 H2558-67, Schelter et al 2006 J. Neurosci. Methods 152 210-9), is to give a broader overview over existing coupling measures and where to look to find the most appropriate tool for a given situation. The review will comprise a test of one representative of the most important coupling measure groups using a simple toy model to illustrate some essential features of the tools. At the end we will summarise the performance of each measure and offer some advice on when to use which method.

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Causality Between the Amplitude and Frequency of Cardiac Oscillations

Cited by 6 publications

References 12 publications

Information Domain Approach to the Investigation of Cardio-Vascular, Cardio-Pulmonary, and Vasculo-Pulmonary Causal Couplings

Information Domain Approach to the Investigation of Cardio-Vascular, Cardio-Pulmonary, and Vasculo-Pulmonary Causal Couplings

Pulse transit times to the capillary bed evaluated by laser Doppler flowmetry

Causality in physiological signals

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