Multimodal signal processing for the analysis of cardiovascular variability

Porta, Alberto; Aletti, Federico; Vallais, Frederic; Baselli, Giuseppe

doi:10.1098/rsta.2008.0229

Cited by 42 publications

(31 citation statements)

References 86 publications

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“…This study falls within the large body of literature on system identification analysis of cardiovascular variability interactions (11,12,14). Among the various identification methods that have been employed, we chose a frequency-domain method (22) to estimate the kernels of the nonlinear models.…”

Section: Discussionmentioning

confidence: 99%

Nonlinear identification of the total baroreflex arc: chronic hypertension model

Moslehpour

Kawada

Sunagawa

et al. 2016

American Journal of Physiology-Regulatory, Integrative and Comp

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. Its linear dynamic functioning has been shown to be preserved in spontaneously hypertensive rats (SHR). However, the system is known to exhibit nonlinear dynamic behaviors. The aim of this study was to establish nonlinear dynamic models of the total arc (and its subsystems) in hypertensive rats and to compare these models with previously published models for normotensive rats. Hypertensive rats were studied under anesthesia. The vagal and aortic depressor nerves were sectioned. The carotid sinus regions were isolated and attached to a servo-controlled piston pump. AP and sympathetic nerve activity were measured while CSP was controlled via the pump using Gaussian white noise stimulation. Second-order, nonlinear dynamics models were developed by application of nonparametric system identification to a portion of the measurements. The models of the total arc predicted AP 21-43% better (P Ͻ 0.005) than conventional linear dynamic models in response to a new portion of the CSP measurement. The linear and nonlinear terms of these validated models were compared with the corresponding terms of an analogous model for normotensive rats. The nonlinear gains for the hypertensive rats were significantly larger than those for the normotensive rats [Ϫ0.38 Ϯ 0.04 (unitless) vs. Ϫ0.22 Ϯ 0.03, P Ͻ 0.01], whereas the linear gains were similar. Hence, nonlinear dynamic functioning of the sympathetically mediated total arc may enhance baroreflex buffering of AP increases more in SHR than normotensive rats. arterial baroreflex; Gaussian white noise; system identification; nonlinear model; hypertension THE CAROTID SINUS BAROREFLEX plays a central role in maintaining arterial pressure (AP) in the face of fast-acting, exogenous disturbances and may also contribute to long-term AP regulation (7,20,21). This system responds to increases in carotid sinus pressure (CSP) by decreasing efferent sympathetic nerve activity (SNA), which, in turn, decreases AP. We refer to the aggregate, open-loop system relating CSP to AP as the total baroreflex arc, the "controller" subsystem relating CSP to SNA as the neural arc, and the "effector" subsystem relating SNA to AP as the peripheral arc.We and others have identified the linear dynamics of the three baroreflex arcs in the form of transfer functions (i.e., gain and phase as a function of frequency) (3-5, 16). These linear models can capture the dynamic behavior of the systems to a significant extent. We previously showed that the linear dynamics of the total arc are preserved in spontaneously hypertensive rats (SHR) despite resetting of mean AP (4). However, the nonlinear dynamics of this system, which have been less investigated, could possibly respond differently to the chronic hypertension model.In a recent study (9), we employed the Gaussian white noise approach for nonlinear system identification to develop a second-order, nonlinear dynamic model of the total arc in normotensive Wistar-Kyoto rats (WKY). The model predicted AP 12% better than a linear dynamic model in response to new Gaussian w...

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Section: Discussionmentioning

confidence: 99%

Nonlinear identification of the total baroreflex arc: chronic hypertension model

Moslehpour

Kawada

Sunagawa

et al. 2016

American Journal of Physiology-Regulatory, Integrative and Comp

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“…In relation to the complexity of the sinus node activity modulation system, a predominantly nonlinear behaviour has to be assumed (Porta et al 2009;Voss et al 2009). Thus, the detailed description and classification of dynamic changes using time and frequency measures are often not sufficient.…”

Section: (D ) Heart Rate Variabilitymentioning

confidence: 99%

Depression of cardiovascular autonomic function is more pronounced after mitral valve surgery: evidence for direct trauma

Retzlaff

Bauernschmitt

Malberg

et al. 2009

Phil. Trans. R. Soc. A.

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The analysis of baroreflex sensitivity (BRS) and heart rate variability (HRV ) leads to additional insights into patients' prognosis after cardiovascular events. The following study was performed to assess the differences in the post-operative recovery of autonomic regulation after mitral valve (MV ) and aortic valve (AV ) surgery with a heart-lung machine. Among the 43 consecutive male patients enrolled in a prospective study, 26 underwent isolated AV surgery and 17 isolated MV surgery. Blood pressure as well as ECG signals were recorded the day before, 24 hours after and one week after surgery. BRS was calculated according to the dual sequence method, and HRV was calculated using standard linear as well as nonlinear parameters. There were no major differences between the two groups in the pre-operative values. At 24 hours a comparable depression of HRV and BRS in both groups was observed, while at 7 days there was partial recovery in AV patients, which was absent in MV patients: p (AV versus MV )!0.001. While the response of the autonomic system to surgery is similar in AV and MV patients, there is obviously a decreased ability to recover in MV patients, probably attributed to traumatic lesions of the autonomic nervous system by opening the atria. Ongoing research is required for further clarification of the pathophysiology of this phenomenon and to establish strategies to restore autonomic function.

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“…the presence of directional interactions among a set of measured variables, is becoming of paramount importance in the study of physiological systems. Application of methods aimed at detecting causality from the analysis of experimental multivariate (MV) time series, indeed, ranges from neurophysiology [1] to cardiovascular physiology [2]. While a universally accepted definition of causality is still lacking, a very popular and practically useful notion is that first proposed by Wiener [3] and then formalized by Granger [4] in the context of linear regression modelling of MV stochastic processes.…”

Section: Introductionmentioning

confidence: 99%

A framework for assessing frequency domain causality in physiological time series with instantaneous effects

Faes

Erla

Porta

et al. 2013

Phil. Trans. R. Soc. A.

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We present an approach for the quantification of directional relations in multiple time series exhibiting significant zero-lag interactions. To overcome the limitations of the traditional multivariate autoregressive (MVAR) modelling of multiple series, we introduce an extended MVAR (eMVAR) framework allowing either exclusive consideration of time-lagged effects according to the classic notion of Granger causality, or consideration of combined instantaneous and lagged effects according to an extended causality definition. The spectral representation of the eMVAR model is exploited to derive novel frequency domain causality measures that generalize to the case of instantaneous effects the known directed coherence (DC) and partial DC measures. The new measures are illustrated in theoretical examples showing that they reduce to the known measures in the absence of instantaneous causality, and describe peculiar aspects of directional interaction among multiple series when instantaneous causality is non-negligible. Then, the issue of estimating eMVAR models from time-series data is faced, proposing two approaches for model identification and discussing problems related to the underlying model assumptions. Finally, applications of the framework on cardiovascular variability series and multichannel EEG recordings are presented, showing how it allows one to highlight patterns of frequency domain causality consistent with well-interpretable physiological interaction mechanisms.

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Multimodal signal processing for the analysis of cardiovascular variability

Cited by 42 publications

References 86 publications

Nonlinear identification of the total baroreflex arc: chronic hypertension model

Nonlinear identification of the total baroreflex arc: chronic hypertension model

Depression of cardiovascular autonomic function is more pronounced after mitral valve surgery: evidence for direct trauma

A framework for assessing frequency domain causality in physiological time series with instantaneous effects

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