Search

Heart auscultation is an inexpensive and fundamental technique to effectively to diagnose cardiovascular disease. However, due to relatively high human error rates even when auscultation is performed by an experienced physician, and due to the not universal availability of qualified personnel e.g. in developing countries, a large body of research is attempting to develop automated, computational tools for detecting abnormalities in heart sounds. The large heterogeneity of achievable data quality and devices, the variety o possible heart pathologies, and a generally poor signal-to-noise ratio make this problem extremely challenging. We present an accurate classification strategy for diagnosing heart sounds based on 1) automatic heart phase segmentation, 2) state-of-the art filters drawn from the filed of speech synthesis (mel-frequency cepstral representation), and 3) an ad-hoc multi-branch, multi-instance artificial neural network based on convolutional layers and fully connected neuronal ensembles which separately learns from each heart phase, hence leveraging their different physiological significance. We demonstrate that it is possible to train our architecture to reach very high performances, e.g. an AUC of 0.87 or a sensitivity of 0.97. Our machine-learning-based tool could be employed for heart sound classification, especially as a screening tool in a variety of situations including telemedicine applications.

The human immunodeficiency virus (HIV) causes an infectious disease with a high viral tropism toward CD4 T-lymphocytes and macrophage. Since the advent of combined antiretroviral therapy (CART), the number of opportunistic infectious disease has diminished, turning HIV into a chronic condition. Nevertheless, HIV-infected patients suffer from several life-long symptoms, including the HIV-associated neurocognitive disorder (HAND), whose biological substrates remain unclear. HAND includes a range of cognitive impairments which have a huge impact on daily patient life. The aim of this study was to examine putative structural brain network changes in HIV-infected patient to test whether diffusion-imaging-related biomarkers could be used to discover and characterize subtle neurological alterations in HIV infection. To this end, we employed multi-shell, multi-tissue constrained spherical deconvolution in conjunction with probabilistic tractography and graph-theoretical analyses. We found several statistically significant effects in both local (right postcentral gyrus, right precuneus, right inferior parietal lobule, right transverse temporal gyrus, right inferior temporal gyrus, right putamen and right pallidum) and global graph-theoretical measures (global clustering coefficient, global efficiency and transitivity). Our study highlights a global and local reorganization of the structural connectome which support the possible application of graph theory to detect subtle alteration of brain regions in HIV patients.Clinical Relevance-Brain measures able to detect subtle alteration in HIV patients could also be used in e.g. evaluating therapeutic responses, hence empowering clinical trials.

Diffusion tensor imaging (DTI) has been used to explore changes in the brain of subjects with human immunodeficiency virus (HIV) infection. However, DTI notoriously suffers from low specificity. Neurite orientation dispersion and density imaging (NODDI) is a compartmental model able to provide specific microstructural information with additional sensitivity/specificity. In this study we use both the NODDI and the DTI models to evaluate microstructural differences between 35 HIV-positive patients and 20 healthy controls. Diffusion-weighted imaging was acquired using three b-values (0, 1000 and 2500 s/mm 2 ). Both DTI and NODDI models were fitted to the data, obtaining estimates for fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), neurite density index (NDI) and orientation dispersion index (ODI), after which we performed group comparisons using Tract-based spatial statistics (TBSS). While significant group effects were found in in FA, MD, RD, AD and NDI, NDI analysis uncovered a much wider involvement of brain tissue in HIV infection as compared to DTI. In region-of interest (ROI)-based analysis, NDI estimates from the right corticospinal tract produced excellent performance in discriminating the two groups (AUC = 0.974, sensitivity = 90%; specificity =97%).

In 2019, approximately 38 million people were living with human immunodeficiency virus (HIV). Combined antiretroviral therapy (cART) has determined a change in the course of HIV infection, transforming it into a chronic condition which results in cumulative exposure to antiretroviral drugs, inflammatory effects and aging. Relatedly, at least one quarter of HIV-infected patients suffer from cognitive, motor and behavioral disorder, globally known as HIV-associated neurocognitive disorders (HAND). In this context, objective, neuroimaging-based biomarkers are therefore highly desirable in order to detect, quantify and monitor HAND in all disease stages. In this study, we employed functional MRI in conjunction with graph-theoretical analysis as well as a newly developed functional brain network disruption index to assess a putative functional reorganization in HIV positive patients. We found that brain function of HIV patients is deeply reorganized as compared to normal controls. Interestingly, the regions in which we found reorganized hubs are integrated into neuronal networks involved in working memory, motor and executive functions often altered in patients with HAND. Overall, our study demonstrates that rs-fMRI combined with advanced graph theoretical analysis and disruption indices is able to detect early, subtle functional changes of brain networks in HIV patients before structural changes become evident.

Primary open angle glaucoma (POAG) is one of the most common causes of permanent blindness in the world. Recent studies have originated the hypothesis that POAG could be considered as a central nervous system pathology which results in secondary visual involvement. The aim of this study is to assess possible structural whole brain connectivity alterations in POAG by combining multi-shell diffusion weighted imaging, multi-shell multi-tissue probabilistic tractography, graph theoretical measures and a newly designed disruption index, which evaluates the global reorganization of brain networks in group-wise comparisons. We found global differences in structural connectivity between Glaucoma patients and controls, as well as in local graph theoretical measures. These changes extended well beyond the primary visual pathway. Furthermore, group-wise and subject-wise disruption indices were found to be statistically different between glaucoma patients and controls, with a positive slope. Overall, our results support the hypothesis of a whole-brain structural reorganization in glaucoma which is specific to structural connectivity, possibly placing this disease within the recently defined groups of brain disconnection syndrome.

Recent reports suggested that even moderate sudden sensorineural hearing loss (SSNHL) can be partly responsible for a loss of gray matter volume in the primary auditory cortex, hence reducing the capacity of the auditory cortical areas to react to sound stimulation. There is also evidence for a plastic reorganization of brain functional networks visible as enhanced local functional connectivity. The aim of this study was to use rs-fMRI, in conjunction with graph- theoretical analysis and a newly developed functional "disruption index" to study whole-brain as well as local functional changes in patients with acute and unilateral sensorineural hearing loss. No statistically significant differences in global or local network measures we found between SSNHL patients and healthy controls. However, when analyzing local metrics through the disruption index k, we found negative values for k which were statistically different from zero both in single subject analysis. Additionally, we found several associations between graph-theoretical metrics and clinical parameters.

While Granger Causality(GC)-based approaches have been widely employed in a vast number of problems in network science, the vast majority of GC applications are based on linear multivariate autoregressive (MVAR) models. However, it is well known that real-life system (and biological networks in particular) exhibit notable nonlinear behavior, hence undermining that validity of MVAR-based approaches to estimating GC (MVAR-GC). In this paper, we define a novel approach to estimating nonlinear, directed within-network interactions based on a specific class of recurrent neural networks (RNN) termed echo-state networks (ESN). We reformulate the classical GC framework in terms of ESN-based models for multivariate signals generated by arbitrarily complex networks, and characterize the ability of our ESN-based Granger Causality (ES-GC) to capture nonlinear causal relations by simulating multivariate coupling in a network of nonlinearly interacting, noisy Duffing oscillators operating in a chaotic regime. Synthetic validation shows a net advantage of ES-GC over all other estimators in detecting nonlinear, causal links. We then explore the structure of EC-GC networks in the human brain in functional MRI data from 1003 healthy subjects scanned at rest at 3T, discovering previously unknown between-network interactions. In summary, ES-GC performs significantly better than commonly used and recently developed GC detection tools, making it a superior tool for the analysis of e.g. multivariate biological networks.

Resting-state functional magnetic resonance imaging (rs-fMRI) is commonly employed to study changes in functional brain connectivity. Recently, the hypothesis of a brain involvement in primary open angle glaucoma has sprung interest for neuroimaging studies in this pathology. The purpose of this study is to evaluate a putative reorganization of brain networks in glaucomatous patients through graph-theoretical measures of integration, segregation and centrality by exploiting a multivariate networks association measure and a recently introduced global and local brain network disruption index. Nineteen glaucoma patients and sixteen healthy control subjects (age: 50 - 76, mean 61 years) underwent rs-fMRI examination at 3T. After preprocessing, rs-fMRI time series were parcellated into 116 regions (AAL atlas), adjacency matrices were computed based on partial correlations and graph-theoretical measures of integration, segregation and centrality as well as group-wise and subject-wise disruption index estimates were generated for all subjects. We found that the group-wise disruption index was negative and statistically different from 0 in for all graph theoretical metrics. Additionally, statistically significant group-wise differences in subject-wise disruption indexes were found in all local metrics. The differences in local network measures highlight cerebral reorganization of brain networks in glaucoma patients, supporting the interpretation of glaucoma as central nervous system disease, likely part of the heterogeneous group of recently described disconnection syndromes.

Breast cancer is one of the most common cancer in women, with more than 1,300,000 cases and 450,000 deaths each year worldwide. Recent studies show that early breast cancer detection, along with suitable treatment, could significantly reduce breast cancer death rates in the long-term. While the consequences of a false positive diagnosis can be psychologically and socioeconomically burdensome, the result of a false negative diagnosis can be devastating, especially in terms of health detriment. In this context, the false positive and false negative rates commonly achieved by radiologists are extremely arduous to estimate and control, and some authors have estimated figures of up to 20% of total diagnoses or more. Novel ideas in computer-assisted diagnosis have been prompted by the introduction of deep learning techniques in general and of convolutional neural networks (CNN) in particular. In this paper, we design and validate an ad-hoc CNN architecture specialized in breast lesion classification and heuristically explore possible parameter combinations and architecture styles in order to propose a model selection criterion which can pose the emphasis on reducing false negatives while still retaining acceptable accuracy. We achieve good classification performance on the validation and test set, demonstrating how an ad-hoc, random initialization CNN architecture can provide practical aid in the classification and staging of breast cancer.

The use of Multivariate Granger Causality (MVGC) in estimating directed Blood-Oxygen-Level- Dependant (BOLD) connectivity is still controversial. This is mostly due to the short data Ienghts typically available in func- tional MRI (fMRI) acquisitions, to the very nature of the BOLD acquisition strategy (which yields extremely low signal- to-noise-ratio) and importantly to the fact that neuronal activi- ty is convolved with a slow-varying haemodynamic response function (HRF) which therefore generates a temporal confound which is arduous to account for when basing MVGC estimates on vector autoregressive models (VAR). In this paper, we em- ploy realistic complex network models based on Izhikevich neuronal populations, interlinked by realistic neuronal fiber bundles which exert compounded directed influences and cas- cade into Baloon-model-like neurovascular coupling, to explore and validate the MVGC approach to directed connectivity es- timation in realistic fMRI conditions and in a complex directed network setting. In particular, we show in silico that the top 1 percentile of a BOLD connectivity matrix estimated with MVGC from BOLD data similar to the one provided by the Human Connectome Project (HCP) has a Positive Predictive Value very close to 1, hence corroborating the evidence that the "strongest" connections can be safely studied with this method in fMRI.

Recently, the field of functional brain connectivity has shifted its attention on studying how functional connectivity (FC) between remote regions changes over time. It is becoming increasingly evident that the human "connectome" is a dynamical entity whose variations are effected over very short timescales and reflect crucial mechanisms which underline the physiological functioning of the brain. In this study, we employ ad-hoc statistical and surrogate data generation methods to quantify whether and which brain networks displayed dynamic behaviors in a very large sample of healthy subjects provided by the Human Connectome Project (HCP). Our findings provided evidences that there are specific pairs of networks and specific networks within the healthy brain that are more likely to display dynamic behaviors. This new set of findings supports the notion that studying the time-variant connectivity in the brain could reveal useful and important properties about brain functioning in health and disease.

It has recently become evident that the functional connectome of the human brain is a dynamical entity whose time evolution carries important information underpinning physiological brain function as well as its disease-related aberrations. While simple sliding window approaches have had some success in estimating dynamical brain connectivity in a functional MRI (fMRI) context, these methods suffer from limitations related to the arbitrary choice of window length and limited time resolution. Recently, Generalized autoregressive conditional heteroscedastic (GARCH) models have been employed to generate dynamical covariance models which can be applied to fMRI. Here, we employ a GARCH-based method (dynamic conditional correlation - DCC) to estimate dynamical brain connectivity in the Human Connectome Project (HCP) dataset and study how the dynamic functional connectivity behaviors related to personality as described by the five-factor model. Openness, a trait related to curiosity and creativity, is the only trait associated with significant differences in the amount of time-variability (but not in absolute median connectivity) of several inter-network functional connections in the human brain. The DCC method offers a novel window to extract dynamical information which can aid in elucidating the neurophysiological underpinning of phenomena to which conventional static brain connectivity estimates are insensitive.

While a large body of research has focused on the study of within-brain physiological networks (i.e. brain connectivity) as well as their disease-related aberration, few investigators have focused on estimating the directionality of these brain-brain interaction which, given the complexity of brain networks, should be properly conditioned in order to avoid the high number of false positives commonly encountered when using bivariate approaches to brain connectivity estimation. Additionally, the constituents of a number of brain subnetworks, and in particular of the central autonomic network (CAN), are still not completely determined. In this study we present and validate a global conditioning approach to reconstructing directed networks using complex synthetic networks of nonlinear oscillators. We then employ our framework, along with a probabilistic model for heartbeat generation, to characterize the directed functional connectome of the human brain and to establish which parts of this connectome effect the directed central modulation of peripheral autonomic cardiovascular control. We demonstrate the effectiveness of our conditioning approach and unveil a top-down directed influence of the default mode network on the salience network, which in turn is seen to be the strongest modulator of directed autonomic cardiovascular control.

In recent years, the study of the human connectome (i.e. of statistical relationships between non spatially contiguous neurophysiological events in the human brain) has been enormously fuelled by technological advances in high-field functional magnetic resonance imaging (fMRI) as well as by coordinated world wide data-collection efforts like the Human Connectome Project (HCP). In this context, Granger Causality (GC) approaches have recently been employed to incorporate information about the directionality of the influence exerted by a brain region on another. However, while fluctuations in the Blood Oxygenation Level Dependent (BOLD) signal at rest also contain important information about the physiological processes that underlie neurovascular coupling and associations between disjoint brain regions, so far all connectivity estimation frameworks have focused on central tendencies, hence completely disregarding so-called in-variance causality (i.e. the directed influence of the volatility of one signal on the volatility of another). In this paper, we develop a framework for simultaneous estimation of both in-mean and in-variance causality in complex networks. We validate our approach using synthetic data from complex ensembles of coupled nonlinear oscillators, and successively employ HCP data to provide the very first estimate of the in-variance connectome of the human brain.

Symptoms of temporal lobe epilepsy (TLE) are frequently associated with autonomic dysregulation, whose underlying biological processes are thought to strongly contribute to sudden unexpected death in epilepsy (SUDEP). While abnormal cardiovascular patterns commonly occur during ictal events, putative patterns of autonomic cardiac effects during pre-ictal (PRE) periods (i.e. periods preceding seizures) are still unknown. In this study, we investigated TLE-related heart rate variability (HRV) through instantaneous, nonlinear estimates of cardiovascular oscillations during inter-ictal (INT) and PRE periods. ECG recordings from 12 patients with TLE were processed to extract standard HRV indices, as well as indices of instantaneous HRV complexity (dominant Lyapunov exponent and entropy) and higher-order statistics (bispectra) obtained through definition of inhomogeneous point-process nonlinear models, employing Volterra-Laguerre expansions of linear, quadratic, and cubic kernels. Experimental results demonstrate that the best INT vs. PRE classification performance (balanced accuracy: 73.91%) was achieved only when retaining the time-varying, nonlinear, and non-stationary structure of heartbeat dynamical features. The proposed approach opens novel important avenues in predicting ictal events using information gathered from cardiovascular signals exclusively.

Nanoparticle (NP) toxicity is determined by a vast number of topological, sterical, physico-chemical as well as biological properties, rendering a priori evaluation of the effect of NP on biological tissue as arduous as it is necessary and urgent. We aimed at mining the HORIZON 2020 MODENA COST NP cytotoxicity database through nonlinear predictive regressor learning systems in order to assess the power of available NP descriptors and assay characteristics in predicting NP toxicity. Specifically, we assessed the results of cytotoxicity assays performed on 57 NP and trained two different nonlinear regressors (Support Vector Regressors [SVR] with polynomical kernels and Radial Basis Function [RBF] regressors) within a nested-cross validation scheme for parameter optimization to predict toxicity as quantified by EC25, EC50 and slope while using the regressional ReliefF algorithm (RReliefF) for feature selection. Available NP attributes were material, coating, cell type, dispersion protocol, shape, 1st and 2nd dimension, aspect ratio, surface area, zeta potential and size in situ. In most regressor learning systems, after feature selection with the RReliefF algorithm, the correlation between real and estimated toxicity endpoint values increased monotonically with the number of included features, reaching values above 0.90. The best performance was obtained with RBF regressors, and the most informative features in predicting toxicity endpoints were related to nanoparticle structure. These trends did not change significantly between toxicity endpoints. In conclusion, EC25, EC50 and slope can be predicted with high correlation using purely data-driven, machine learning methods in Adenosine triphosphate (ATP)-based NP cytotoxicity assays.

Classical multivariate approaches based on Granger causality (GC) which estimate functional connectivity in the brain are almost exclusively based on autoregressive models. Nevertheless, information available from past samples is limited due to both signal autocorrelation and necessarily low model orders. Consequently, multiple time-scales interactions are usually unaccounted for. To overcome these limitations, in this study we propose the use of discrete-time orthogonal Laguerre basis functions within a Wiener-Volterra decomposition of the BOLD signals to perform effective GC assessments of brain functional connectivity. We validate our method in synthetic noisy oscillator networks, and analyze experimental fMRI data from 30 healthy subjects publicly available within the Human Connectome Project (HCP). Synthetic results demonstrate that our Laguerre-Volterra based GC estimates outperform classical approaches in terms of accuracy in detecting true causal links while rejecting false causal links in complex nonlinear networks. Human data analysis shows for the first time that the default mode network modulates both the salience network as well as fronto-temporal circuits in a causal fashion.

Diffusion tensor imaging (DTI) provides exquisite sensitivity to structural and microstructural characteristics of brain tissue, and is routinely employed in advanced neuroimaging applications. DTI is commonly performed using intrinsically noisy echo-planar imaging techniques and poses high demands both on scanner performance and on in-scanner subject time, which in turn is directly related to the number of diffusion-weighting direction one requires. While DTI-derived indices such as fractional anisotropy (FA), diffusion tensor trace and anisotropy mode have proven extremely useful in characterizing disease-related aberrations, their estimation is commonly performed using fitting routines that do not properly take into account MRI noise distribution. In this paper, we present a distribution-aware maximum likelihood tensor estimation framework which also allows, for the first time, separate local noise estimation in both diffusion weighted and reference images. We validate our framework using multiple water phantom diffusion weighted acquisitions, and demonstrate its feasibility in human data. We then employ our framework within Monte Carlo simulations to show how the minimum achievable uncertainty attainable in DTI depends on signal-to-noise ratio (SNR) and number of diffusion gradient directions, demonstrating that these dependencies could be recast into simple power laws which may serve as guidelines for application-specific DTI protocol design.

Liver transplantation remains the only curative treatment option for a variety of end-stage liver diseases. Prediction of major adverse events following surgery has traditionally focused on static predictors that are known prior to surgery. The effects of intraoperative management can now be explored due to the archiving of high-resolution monitoring data. We extracted intraoperative hemodynamic trend data of 55 patients undergoing orthotopic liver transplantation (OLT) and computed 12 features from the systolic arterial blood pressure (ABP), cardiac index, central venous pressure (CVP), and stroke volume variation (SVV) signals. Using a logistic regression classifier with a leave-one-out cross-validation procedure, we selected subsets of these features to predict mortality up to 180 days after surgery. Best performance was achieved with a combination of 3 features - median absolute deviation (MAD) of ABP, median CVP, and time spent with SVV <; 10% - reaching an area under the receiver-operating characteristic (or c-statistic) of 0.808. Odds ratios (OR) computed from the coefficients of the multivariate logistic regression model constructed from these features showed that greater time spent with SVV <; 10% (OR = 0.981 min(-1), p = 0.001) and greater MAD of systolic ABP (OR = 0.696 mmHg(-1), p = 0.026) were significantly associated with survival. Adding preoperative measures such as age and serum concentrations of albumin, bilirubin, and creatinine failed to improve performance of the prediction model. These results show that the course of intraoperative hemodynamics can predict 180-day mortality after OLT.

We used 7T fMRI with simultaneous physiological signals acquisitions to investigate the causal interactions from resting state brain activity to autonomic nervous system (ANS) outflow as quantified through a probabilistic heartbeat model. Given the highly redundant nature of brain-derived signals, we compare the results of traditional bivariate Granger Causality (GC) to a globally conditioned approach which evaluates the additional influence of each brain region on ANS activity while factoring out effects concomitantly mediated by other brain regions. The bivariate approach results in an unrealistically large number of spurious causal brain-heart links. In contrast, using the globally conditioned approac, we demonstrate the existence of significant selective causal links between cortical/subcortical brain regions and ANS outflow for sympathetic and parasympathetic modulation as well as sympathovagal balance, with a prominent involvement of frontal, parietal, and cerebellar regions and Sensory Motor, Default Mode, Left and Right executive networks. Provided proper conditioning is employed to eliminate spurious causalities, 7T functional imaging coupled with physiological signal acquisition and GC analysis is able to quantify directed brain-heart interactions reflecting central modulation of ANS outflow.

The majority of studies on fluid responsiveness is focused on volume expansion maneuvers in intensive care unit (ICU), while fewer studies have analyzed the same problem during major surgery. Among them, the results are contrasting. The aim of this work was to compare the performance of different hemodynamic indices in the prediction of cardiac output variations following fast fluid infusion. The study was limited to a particular type of major surgery, i.e. liver transplantation and hepatectomy. Our results showed that pulse pressure variation (PPV) estimated according to the definition, i.e. within single respiratory cycles, and PPV estimated by PiCCO monitor system are coherent and very similar. Moreover, PPV and stroke volume variation (SVV) produced good values of sensitivity and specificity in separating the subjects into responsive and non responsive to maneuvers.

This paper presents the analysis of autonomic nervous system (ANS) control of heart rate (HR) and of cardiac baroreflex sensitivity (BRS) in patients undergoing general anesthesia for major surgery through spectral analysis techniques and with the Granger causality approach that take into account the causal relationships between HR and arterial blood pressure (ABP) variability. Propofol produced a general decrease in ABP due to its vasodilatory effects, a reduction in BRS, while HR remained unaltered with respect to baseline values before induction of anesthesia. The bivariate model suggests that the feedback pathway of cardiac baroreflex could be blunted by propofol induced anesthesia and that the feedforward pathway could be unaffected by anesthesia.

Transcranial Magnetic Stimulation offers enormous potential for noninvasive brain stimulation. While it is known that brain tissue significantly "reshapes" induced field and charge distributions, most modeling investigations to-date have focused on single-subject data with limited generality. Further, the effects of the significant uncertainties which exist in the simulation (i.e. brain conductivity distributions) and stimulation (e.g. coil positioning and orientations) setup have not been quantified. In this study, we construct a high-resolution anisotropic head model in standard ICBM space, which can be used as a population-representative standard for bioelectromagnetic simulations. Further, we employ Monte-Carlo simulations in order to quantify how uncertainties in conductivity values propagate all the way to induced field and currents, demonstrating significant, regionally dependent dispersions in values which are commonly assumed "ground truth". This framework can be leveraged in order to quantify the effect of any type of uncertainty in noninvasive brain stimulation and bears relevance in all applications of TMS, both investigative and therapeutic.

In critical care patient management, extensive and invasive patient monitoring is routinely performed in order to quantify patient status in view of therapeutic interventions. Little quantitative integration is performed when collecting information from multiple monitors, and processing algorithms are often based on little physiological understanding. Mechanistic modeling can offer insight into the mechanisms underlying patient stability and sensitivity to alterations in physiological variables. Starting from existing models, we construct an integrated model which combines detailed neural cardiovascular regulation with realistic circulation modeling, using Monte-Carlo techniques for reparameterisation when merging the two models. The combined model is analyzed in terms of its dynamical stability and sensitivity to parameter perturbations under simulated conditions of fluid deficit, anaesthesia, and dilatative cardiomyopathy. The results exemplify how a structural model can serve as a quantitative guide in assessing how different underlying patient states can alter the haemodynamics impact of external therapeutic intervention.

This paper presents the analysis of the autonomic nervous system (ANS) control and cardiac baroreflex sensitivity in patients undergoing general anesthesia for major surgery, with the goal of evaluating the effects of anesthesia bolus induction with propofol on autonomic control of heart rate (HR) and arterial blood pressure (ABP). The increase in baroreflex gain in the LF band observed through two different methods hints at the fact that the baroreflex may increase heart period (HP) following a transient ABP decrease, but its response displays a larger amplitude, to compensate for the blunting of the sympathetic action on heart rate and vascular resistance.

In this paper, we propose the use of black box models for the system identification of the cardiopulmonary baroreflex control of arterial resistance and of ventricular contractility and of arterial baroreflex control of heart rate (HR) from invasive, continuous measurements of arterial blood pressure (ABP) and central venous pressure (CVP), and non invasive, continuous recordings of ECG and respiration. Two crucial phases of the abdominal aortic aneurism (AAA) repair were investigated: the clamping and declamping of aorta. The objective of the present work is to evaluate and to test the ability to monitor baroreflex responses to clamping and declamping maneuvers preceding and following aneurism removal.