Cardiovascular Dynamics Research Articles

Impact of Force Exercise on Cardiovascular Dynamics

Impact of Force Exercise on Cardiovascular Dynamics

Label-free quantitative measurement of cardiovascular dynamics in a zebrafish embryo using frequency-comb-referenced-quantitative phase imaging.

.Significance: Real-time monitoring of the heart rate and blood flow is crucial for studying cardiovascular dysfunction, which leads to cardiovascular diseases.Aim: This study aims at in-depth understanding of high-speed cardiovascular dynamics in a zebrafish embryo model for various biomedical applications via frequency-comb-referenced quantitative phase imaging (FCR-QPI).Approach: Quantitative phase imaging (QPI) has emerged as a powerful technique in the field of biomedicine but has not been actively applied to the monitoring of circulatory/cardiovascular parameters, due to dynamic speckles and low frame rates. We demonstrate FCR-QPI to measure heart rate and blood flow in a zebrafish embryo. FCR-QPI utilizes a high-speed photodetector instead of a conventional camera, so it enables real-time monitoring of individual red blood cell (RBC) flow.Results: The average velocity of zebrafish’s RBCs was measured from 192.5 to at 24 to 28 hour-post-fertilization (hpf). In addition, the number of RBCs in a pulsatile blood flow was revealed to 16 cells/pulse at 48 hpf. The heart rates corresponded to 94 and 142 beats-per-minute at 24 and 48 hpf.Conclusions: This approach will newly enable in-depth understanding of the cardiovascular dynamics in the zebrafish model and possible usage for drug discovery applications in biomedicine.

Assessment of Sepsis in the ICU by Linear and Complex Characterization of Cardiovascular Dynamics.

Sepsis is one of the pathological conditions with the highest incidence in intensive care units. Sepsis-induced cardiac and autonomic dysfunction are well-known effects, among others, caused by a dysregulated host response to infection. In this context, we investigate the role of complex cardiovascular dynamics quantified through sample entropy indices from the inter-beat interval, systolic and diastolic blood pressure time series as well as the cross-entropy between heartbeat and systolic blood pressure in patients with sepsis in the first hour of intensive care when compared with non-septic subjects. Results show a significant (p<0.05) reduction in the probability of being septic for a unitary increase in entropy for systolic and diastolic time series (odds equal to 0.038 and 0.264, respectively) when adjusting for confounding factors. A significant (p<0.001) odds ratio (0.248) is observed also in cross-entropy, showing a reduced probability of being septic for an increase in heartbeat and systolic pressure asynchrony. The inclusion of our measures of complexity also determines an increase in the predictive ability (+0.03) of a logistic regression model reaching an area under the receiving operating and precision recall curves both equal to 0.95.Clinical relevance The study demonstrates the ability of information theory in catching a reduction of complex cardiovascular dynamics from vital signs commonly recorded in ICU. The considered complexity measures contribute to characterize sepsis development by showing a general loss of the interaction between heartbeat and pressure regulation. The extracted measures also improve the ability to identify sepsis in the first hour of intensive care.

Analysis of Heart Rate Variability in Normal and Diabetic ECG signals using Fragmentation Approach.

In this work, an attempt is made to quantify the dynamics of the heart rate variability timeseries in normal and diabetic population using fragmentation metrics. ECG signals recorded during deep breathing and head tilt up experiments are utilized for this study. The QRS-wave of ECG is extracted using the Pan Tompkins Algorithm. Heart rate variability features such as heart rate, Percentage of Inflection Points (PIP) and Inverse of the Average Length of the acceleration/deceleration Segment (IALS) are extracted to quantify the variation in signal dynamics. The results indicate that the ECG signals and heart rate variability signals obtained in deep breathing and tilt exhibit varied characteristics in both normal and diabetics. Further, in the diabetic condition the fragmentation measures exhibit a higher value in both deep breathing and tilt which indicates increased alternations in the signal. Most of the extracted fragmentation features are statistically significant (p<0.005) in differentiating normal and diabetic population. It appears that this method of analysis has potential towards the development of systems for the noninvasive assessment of diabetes.Clinical Relevance- This establishes a technique to quantify the variation in cardiovascular dynamics in normal and diabetic population.

An Intra-Cycle Optimal Control Framework for Ventricular Assist Devices Based on Atrioventricular Plane Displacement Modeling

A promising treatment for congestive heart failure is the implementation of a left ventricular assist device (LVAD) that works as a mechanical pump. Modern LVADs work with adjustable constant rotor speed and provide therefore continuous blood flow; however, recently undertaken efforts try to mimic pulsatile blood flow by oscillating the pump speed. This work proposes an algorithmic framework to construct and evaluate optimal pump speed policies with respect to generic objectives. We use a model that captures the atrioventricular plane displacement, which is a physiological indicator for heart failure. We employ mathematical optimization to adapt this model to patient specific data and to find optimal pump speed policies with respect to ventricular unloading and aortic valve opening. To this end, we reformulate the cardiovascular dynamics into a switched system and thereby reduce nonlinearities. We consider system switches that stem from varying the constant pump speed and that are state dependent such as valve opening or closing. As a proof of concept study, we personalize the model to a selected patient with respect to ventricular pressure. The model fitting results in a root-mean-square deviation of about 6 mmHg. The optimization that considers aortic valve opening and ventricular unloading results in speed modulation akin to counterpulsation. These in silico findings demonstrate the potential of personalized hemodynamical optimization for the LVAD therapy.

A mathematical model of cardiovascular dynamics for the diagnosis and prognosis of hemorrhagic shock.

A variety of mathematical models of the cardiovascular system have been suggested over several years in order to describe the time-course of a series of physiological variables (i.e. heart rate, cardiac output, arterial pressure) relevant for the compensation mechanisms to perturbations, such as severe haemorrhage. The current study provides a simple but realistic mathematical description of cardiovascular dynamics that may be useful in the assessment and prognosis of hemorrhagic shock. The present work proposes a first version of a differential-algebraic equations model, the model dynamical ODE model for haemorrhage (dODEg). The model consists of 10 differential and 14 algebraic equations, incorporating 61 model parameters. This model is capable of replicating the changes in heart rate, mean arterial pressure and cardiac output after the onset of bleeding observed in four experimental animal preparations and fits well to the experimental data. By predicting the time-course of the physiological response after haemorrhage, the dODEg model presented here may be of significant value for the quantitative assessment of conventional or novel therapeutic regimens. The model may be applied to the prediction of survivability and to the determination of the urgency of evacuation towards definitive surgical treatment in the operational setting.

Hemodynamic Monitoring in Sepsis-A Conceptual Framework of Macro- and Microcirculatory Alterations.

Circulatory failure in sepsis is common and places a considerable burden on healthcare systems. It is associated with an increased likelihood of mortality, and timely recognition is a prerequisite to ensure optimum results. While there is consensus that aggressive source control, adequate antimicrobial therapy and hemodynamic management constitute crucial determinants of outcome, discussion remains about the best way to achieve each of these core principles. Sound cardiovascular support rests on tailored fluid resuscitation and vasopressor therapy. To this end, an overarching framework to improve cardiovascular dynamics has been a recurring theme in modern critical care. The object of this review is to examine the nature of one such framework that acknowledges the growing importance of adaptive hemodynamic support combining macro- and microhemodynamic variables to produce adequate tissue perfusion.

Distance to Healthy Metabolic and Cardiovascular Dynamics From Fetal Heart Rate Scale-Dependent Features in Pregnant Sheep Model of Human Labor Predicts the Evolution of Acidemia and Cardiovascular Decompensation.

The overarching goal of the present work is to contribute to the understanding of the relations between fetal heart rate (FHR) temporal dynamics and the well-being of the fetus, notably in terms of predicting the evolution of lactate, pH and cardiovascular decompensation (CVD). It makes uses of an established animal model of human labor, where 14 near-term ovine fetuses subjected to umbilical cord occlusions (UCO) were instrumented to permit regular intermittent measurements of metabolites lactate and base excess, pH, and continuous recording of electrocardiogram (ECG) and systemic arterial blood pressure (to identify CVD) during UCO. ECG-derived FHR was digitized at the sampling rate of 1,000 Hz and resampled to 4 Hz, as used in clinical routine. We focused on four FHR variability features which are tunable to temporal scales of FHR dynamics, robustly computable from FHR sampled at 4 Hz and within short-time sliding windows, hence permitting a time-dependent, or local, analysis of FHR which helps dealing with signal noise. Results show the sensitivity of the proposed features for early detection of CVD, correlation to metabolites and pH, useful for early acidosis detection and the importance of coarse time scales (2.5–8 s) which are not disturbed by the low FHR sampling rate. Further, we introduce the performance of an individualized self-referencing metric of the distance to healthy state, based on a combination of the four features. We demonstrate that this novel metric, applied to clinically available FHR temporal dynamics alone, accurately predicts the time occurrence of CVD which heralds a clinically significant degradation of the fetal health reserve to tolerate the trial of labor.

Numerical simulation of electrical network left ventricular circulatory system with biological valve motion model

This paper presents a new concentrated parameter model represented by an electrical network model for cardiovascular dynamics that includes an innovative model of biological heart valve dynamics. We obtained the hemodynamic waveform of the heart by solving the equations of state through programming. The results show that using the biological valve motion model can reflect the phenomenon of blood return flow that is not found in the ideal diode model. This model can simulate the biological characteristics of heart valve reasonably and accurately, and it is simple and easy to control.

The Effect of the Transfer Matrix Applied to MIMO Systems in the Analysis of Cardiovascular Dynamics

The Effect of the Transfer Matrix Applied to MIMO Systems in the Analysis of Cardiovascular Dynamics

Perioperative fluid management-Goal Directed Therapy (GDT)

Perioperative goal-directed hemodynamic therapy is a protocolized treatment strategy aimed at optimization of global cardiovascular dynamics, including oxygen delivery to tissues and organ perfusion pressure. This is achieved by titrating fluids, vasopressors, and inotropes to predefined physiological target values of hemodynamic variables. Its scope is to reduce complications (acute kidney disease, pulmonary oedema, respiratory distress syndrome, wound infections), decrease major abdominal and systemic postoperative complications, length of stay and postoperative morbidity and mortality mainly in high-risk patients undergoing major surgery. Identifying patients in whom perioperative goal-directed hemodynamic therapy can actually improve postoperative outcomes is crucial. This is a review focusing on all the aspects of GDFT compared to standard fluid therapy during surgery.

On the Periodicity of Cardiovascular Fluid Dynamics Simulations.

Three-dimensional cardiovascular fluid dynamics simulations typically require computation of several cardiac cycles before they reach a periodic solution, rendering them computationally expensive. Furthermore, there is currently no standardized method to determine whether a simulation has yet reached that periodic state. In this work, we propose the use of an asymptotic error measurement to quantify the difference between simulation results and their ideal periodic state using open-loop lumped-parameter modeling. We further show that initial conditions are crucial in reducing computational time and develop an automated framework to generate appropriate initial conditions from a one-dimensional model of blood flow. We demonstrate the performance of our initialization method using six patient-specific models from the Vascular Model Repository. In our examples, our initialization protocol achieves periodic convergence within one or two cardiac cycles, leading to a significant reduction in computational cost compared to standard methods. All computational tools used in this work are implemented in the open-source software platform SimVascular. Automatically generated initial conditions have the potential to significantly reduce computation time in cardiovascular fluid dynamics simulations.

Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network.

Cine cardiac magnetic resonance imaging (MRI) is widely used for the diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a promising approach for cardiac MRI motion artifact reduction. In this paper, we propose a novel recurrent generative adversarial network model for cardiac MRI motion artifact reduction. This model utilizes bi-directional convolutional long short-term memory (ConvLSTM) and multi-scale convolutions to improve the performance of the proposed network, in which bi-directional ConvLSTMs handle long-range temporal features while multi-scale convolutions gather both local and global features. We demonstrate a decent generalizability of the proposed method thanks to the novel architecture of our deep network that captures the essential relationship of cardiovascular dynamics. Indeed, our extensive experiments show that our method achieves better image quality for cine cardiac MRI images than existing state-of-the-art methods. In addition, our method can generate reliable missing intermediate frames based on their adjacent frames, improving the temporal resolution of cine cardiac MRI sequences.

Characterization of the Insular Role in Cardiac Function through Intracranial Electrical Stimulation of the Human Insula

The link between brain function and cardiovascular dynamics is an important issue yet to be elucidated completely. The insula is a neocortical brain area that is thought to have a cardiac chronotropic regulatory function, but its role in cardiac contractility is unknown. We aimed to analyze the variability in heart rate and cardiac contractility after functional activation of different insular regions through direct electrical stimulation (E-stim) in humans. This was an observational, prospective study, including patients admitted for stereo-electroencephalographic recording because of refractory epilepsy, in whom the insular cortex was implanted. Patients with anatomical or electrophysiological insular abnormalities and those in whom E-stim produced subjective symptoms were excluded. Variations in heart rate (HR), stroke volume (SV), and cardiac output (CO) were analyzed during insular E-stim and compared with control E-stim of non-eloquent brain regions and sham stimulations. Ten patients were included, 5 implanted in the right insula (52 E-stim) and 5 in the left (37 E-stim). Demographic and clinical characteristics of both groups were similar. E-stim of both right and left insulas induced a significant decrease of the CO and HR, and an increase of the SV. E-stim of control electrodes and sham stimulations were not associated with variations in cardiac function. Blood pressure and respiratory rate remained unaltered. Our results suggest a direct chronotropic and inotropic cardiac depressor function of the right and left insulas. The evidence of an insular direct cardiac regulatory function might open a path in the prevention or treatment of heart failure, arrhythmias, and sudden unexpected death in epilepsy. ANN NEUROL 2021;89:1172-1180.

Toward brain–heart computer interfaces: a study on the classification of upper limb movements using multisystem directional estimates

Objective. Brain–computer interfaces (BCIs) exploit computational features from brain signals to perform a given task. Despite recent neurophysiology and clinical findings indicating the crucial role of functional interplay between brain and cardiovascular dynamics in locomotion, heartbeat information remains to be included in common BCI systems. In this study, we exploit the multidimensional features of directional and functional interplay between electroencephalographic and heartbeat spectra to classify upper limb movements into three classes. Approach. We gathered data from 26 healthy volunteers that performed 90 movements; the data were processed using a recently proposed framework for brain–heart interplay (BHI) assessment based on synthetic physiological data generation. Extracted BHI features were employed to classify, through sequential forward selection scheme and k-nearest neighbors algorithm, among resting state and three classes of movements according to the kind of interaction with objects. Main results. The results demonstrated that the proposed brain–heart computer interface (BHCI) system could distinguish between rest and movement classes automatically with an average 90% of accuracy. Significance. Further, this study provides neurophysiology insights indicating the crucial role of functional interplay originating at the cortical level onto the heart in the upper limb neural control. The inclusion of functional BHI insights might substantially improve the neuroscientific knowledge about motor control, and this may lead to advanced BHCI systems performances.

Quantifying multidimensional control mechanisms of cardiovascular dynamics during multiple concurrent stressors.

Heartbeat regulation is achieved through different routes originating from central autonomic network sources, as well as peripheral control mechanisms. While previous studies successfully characterized cardiovascular regulatory mechanisms during a single stressor, to the best of our knowledge, a combination of multiple concurrent elicitations leading to the activation of different autonomic regulatory routes has not been investigated yet. Therefore, in this study, we propose a novel modeling framework for the quantification of heartbeat regulatory mechanisms driven by different neural routes. The framework is evaluated using two heartbeat datasets gathered from healthy subjects undergoing physical and mental stressors, as well as their concurrent administration. Experimental results indicate that more than 70% of the heartbeat regulatory dynamics is driven by the physical stressor when combining physical and cognitive/emotional stressors. The proposed framework provides quantitative insights and novel perspectives for neural activity on cardiac control dynamics, likely highlighting new biomarkers in the psychophysiology and physiopathology fields. A Matlab implementation of the proposed tool is available online.

A physiological model of the inflammatory-thermal-pain-cardiovascular interactions during an endotoxin challenge.

Inflammation in response to bacterial endotoxin challenge impacts physiological functions, including cardiovascular, thermal and pain dynamics, although the mechanisms are poorly understood. We develop an innovative mathematical model incorporating interaction pathways between inflammation and physiological processes observed in response to an endotoxin challenge. We calibrate the model to individual data from 20subjects in an experimental study of the human inflammatory and physiological responses to endotoxin, and we validate the model against human data from an independent study. Using the model to simulate patient responses to different treatment modalities reveals that a multimodal treatment combining several therapeutic strategies gives the best recovery outcome. Uncontrolled, excessive production of pro-inflammatory mediators from immune cells and traumatized tissues can cause systemic inflammatory conditions such as sepsis, one of the ten leading causes of death in the USA, and one of the three leading causes of death in the intensive care unit. Understanding how inflammation affects physiological processes, including cardiovascular, thermal and pain dynamics, can improve a patient's chance of recovery after an inflammatory event caused by surgery or a severe infection. Although the effects of the autonomic response on the inflammatory system are well-known, knowledge about the reverse interaction is lacking. The present study develops a mathematical model analyzing the inflammatory system's interactions with thermal, pain and cardiovascular dynamics in response to a bacterial endotoxin challenge. We calibrate the model with individual data from an experimental study of the inflammatory and physiological responses to a one-time administration of endotoxin in 20healthy young men and validate it against data from an independent endotoxin study. We use simulation to explore how various treatments help patients exposed to a sustained pathological input. The treatments explored include bacterial endotoxin adsorption, antipyretics and vasopressors, as well as combinations of these. Our findings suggest that the most favourable recovery outcome is achieved by a multimodal strategy, combining all three interventions to simultaneously remove endotoxin from the body and alleviate symptoms caused by the immune system as it fights the infection.

Energetic savings and cardiovascular dynamics of a marine euryhaline fish (Myoxocephalus scorpius) in reduced salinity

Few studies have addressed how reduced water salinity affects cardiovascular and metabolic function in marine euryhaline fishes, despite its relevance for predicting impacts of natural salinity variations and ongoing climate change on marine fish populations. Here, shorthorn sculpin (Myoxocephalus scorpius) were subjected to different durations of reduced water salinity from 33 to 15 ppt. Routine metabolic rate decreased after short-term acclimation (4–9 days) to 15 ppt, which corresponded with similar reductions in cardiac output. Likewise, standard metabolic rate decreased after acute transition (3 h) from 33 to 15 ppt, suggesting a reduced energetic cost of osmoregulation at 15 ppt. Interestingly, gut blood flow remained unchanged across salinities, which contrasts with previous findings in freshwater euryhaline teleosts (e.g., rainbow trout) exposed to different salinities. Although plasma osmolality, [Na+], [Cl−] and [Ca2+] decreased in 15 ppt, there were no signs of cellular osmotic stress as plasma [K+], [hemoglobin] and hematocrit remained unchanged. Taken together, our data suggest that shorthorn sculpin are relatively weak plasma osmoregulators that apply a strategy whereby epithelial ion transport mechanisms are partially maintained across salinities, while plasma composition is allowed to fluctuate within certain ranges. This may have energetic benefits in environments where salinity naturally fluctuates, and could provide shorthorn sculpin with competitive advantages if salinity fluctuations intensify with climate change in the future.

Simulating Dynamics of Circulation in the Awake State and Different Stages of Sleep Using Non-autonomous Mathematical Model With Time Delay.

We propose a mathematical model of the human cardiovascular system. The model allows one to simulate the main heart rate, its variability under the influence of the autonomic nervous system, breathing process, and oscillations of blood pressure. For the first time, the model takes into account the activity of the cerebral cortex structures that modulate the autonomic control loops of blood circulation in the awake state and in various stages of sleep. The adequacy of the model is demonstrated by comparing its time series with experimental records of healthy subjects in the SIESTA database. The proposed model can become a useful tool for studying the characteristics of the cardiovascular system dynamics during sleep.

Analysis of the effect of sitting postures in a vehicle compartment on cardiovascular dynamics.

Analysis of the effect of sitting postures in a vehicle compartment on cardiovascular dynamics.