Multivariate Decomposition of Arterial Blood Pressure Variability for the Assessment of Arterial Control of Circulation

In order to analyze the information carried by arterial blood pressure (ABP) variability, a multivariate parametric model of interactions involving systolic ABP (SAP), diastolic ABP (DAP), pulse pressure (PP), heart period (HP), and respiration is proposed. The model defines SAP as sum of the preceding DAP and PP values; DAP model accounts for arterial baroreflex, diastolic runoff; PP reflects changes in stroke volume related to respiration and HP, afterload; equation residuals reveal other vascular and cardiac output modulations. The model was applied to data from nine young volunteers (aged 29plusmn6 years) during supine cycling at 10%, 20%, and 30% of their maximum effort. Significant basal values and changes across the epochs of the experiment were found in all hemodynamic parameters describing fast, beat-by-beat responses; in SAP and PP total power, DAP low- and high-frequency power (LF, HF), PP very low frequency (VLF), and LF and HF power. A primary role of vascular control through DAP and PP was emphasized by the considered feedbacks and the model residuals. The model proved to be able to assess beat-by-beat cardiovascular interactions and offer a comprehensive view of arterial tree control.     

Multimodal signal processing for the analysis of cardiovascular variability

Cardiovascular (CV) variability as a primary vital sign carrying information about CV regulation systems is reviewed by pointing out the role of the main rhythms and the various control and functional systems involved. The high complexity of the addressed phenomena fosters a multimodal approach that relies on data analysis models and deals with the ongoing interactions of many signals at a time. The importance of closed-loop identification and causal analysis is remarked upon and basic properties, application conditions and methods are recalled. The need of further integration of CV signals relevant to peripheral and systemic haemodynamics, respiratory mechanics, neural afferent and efferent pathways is also stressed.     

A reduced‐order representation of the Poincaré–Steklov operator: an application to coupled multi‐physics problems

This work investigates a model reduction method applied to coupled multi‐physics systems. The case in which a system of interest interacts with an external system is considered. An approximation of the Poincaré–Steklov operator is computed by simulating, in an offline phase, the external problem when the inputs are the Laplace–Beltrami eigenfunctions defined at the interface. In the online phase, only the reduced representation of the operator is needed to account for the influence of the external problem on the main system. An online basis enrichment is proposed in order to guarantee a precise reduced‐order computation. Several test cases are proposed on different fluid–structure couplings. Copyright © 2016 John Wiley & Sons, Ltd.