Arterial Pressure Signals Research Articles

Long-term exposure to ambient air pollution and measures of central hemodynamics and arterial stiffness among multiethnic Chicago residents

ObjectivesTo examine whether long-term air pollution exposure is associated with central hemodynamic and brachial artery stiffness parameters.MethodsWe assessed central hemodynamic parameters including central blood pressure, cardiac parameters, systemic vascular compliance and resistance, and brachial artery stiffness measures [including brachial artery distensibility (BAD), compliance (BAC), and resistance (BAR)] using waveform analysis of the arterial pressure signals obtained from a standard cuff sphygmomanometer (DynaPulse2000A, San Diego, CA). The long-term exposures to particles with an aerodynamic diameter < 2.5 μm (PM2.5) and nitrogen dioxide (NO2) for the 3-year periods prior to enrollment were estimated at residential addresses using fine-scale intra-urban spatiotemporal models. Linear mixed models adjusted for potential confounders were used to examine associations between air pollution exposures and health outcomes.ResultsThe cross-sectional study included 2,387 Chicago residents (76% African Americans) enrolled in the ChicagO Multiethnic Prevention And Surveillance Study (COMPASS) during 2013–2018 with validated address information, PM2.5 or NO2, key covariates, and hemodynamics measurements. We observed long-term concentrations of PM2.5 and NO2 to be positively associated with central systolic, pulse pressure and BAR, and negatively associated with BAD, and BAC after adjusting for relevant covariates. A 1-µg/m3 increment in preceding 3-year exposures to PM2.5 was associated with 1.8 mmHg higher central systolic (95% CI: 0.98, 4.16), 1.0 mmHg higher central pulse pressure (95% CI: 0.42, 2.87), a 0.56%mmHg lower BAD (95% CI: -0.81, -0.30), and a 0.009 mL/mmHg lower BAC (95% CI: -0.01, -0.01).ConclusionThis population-based study provides evidence that long-term exposures to PM2.5 and NO2 is related to central BP and arterial stiffness parameters, especially among African Americans.

Pharmacokinetics and pharmacodynamics of standard nerve agent medical countermeasures in Göttingen Minipigs

Animal research continues to serve a critical role in the testing and development of medical countermeasures. The Göttingen minipig, developed for laboratory research, may provide many benefits for addressing research questions within chemical defense. Targeted development of the Göttingen minipig model could reduce reliance upon non-human primates, and improve study design, statistical power, and throughput to advance medical countermeasures for regulatory approval and fielding. In this vein, we completed foundational pharmacokinetics and physiological safety studies of intramuscularly administered atropine sulfate, pralidoxime chloride (2-PAM), and diazepam across a broad range of doses (1–6 autoinjector equivalent) using adult male Göttingen minipigs (n=11; n=4–8/study) surgically implanted with vascular access ports and telemetric devices to monitor cardiovascular, respiratory, arterial pressure, and temperature signals. Pharmacokinetic data were orderly and the concentration maximum mirrored available human data at comparably scaled doses clearly for atropine, moderately for 2-PAM, and poorly for diazepam. Time to peak concentration approximated 2, 7, and 20 min for atropine, 2-PAM, and diazepam, respectively, and the elimination half-life of these drugs approximated 2 hr (atropine), 3 hr (2-PAM), and 8 hr (diazepam). Atropine sulfate dose-dependently increased the magnitude and duration of tachycardia and decreased the PR and ST intervals (consistent with findings obtained from other species). Mild hypothermia was observed at the highest diazepam dose. Göttingen minipigs appear to provide a ready and appropriate large animal alternative to non-human primates, and further development and evaluation of novel nerve agent medical countermeasures and treatment strategies in this model are justified.

Cerebral autoregulation metrics correlate with some major morbidity and mortality outcomes after cardiac surgery

Background: Personalized arterial blood pressure management based on cerebral autoregulation has shown clinical relevance in cardiac surgery. Here we explore the associations between lower limit of cerebral autoregulation (LLA) metrics and postoperative outcomes in a heterogenous and high-acuity population of patients undergoing high-risk cardiovascular surgery. Methods: Autoregulation parameters were determined retrospectively in 686 patients undergoing cardiac surgery at UAB Hospital. Cerebral oximetry index (COx) and hemoglobin volume reactivity index (HVx) determine a patient’s unique LLA by examining the correlation between mean arterial pressure (MAP) and near-infrared spectroscopy signals. Logistic regression analyses explored associations of area under the time-pressure curve and duration below LLA with stroke, acute kidney injury, operative mortality, low cardiac output syndrome, and mechanical ventilation lasting more than 48 hours. Any incidence of these outcomes was considered as the primary outcome of interest: major morbidity or operative mortality (MMOM). Results: We explored the association of Area under the time-pressure curve for pressures less than patient-specific Lower Limits of Autoregulation (AUCABP&lt;LLA: and MMOM. Results are reported with odds ratio (OR) and 95% confidence intervals (CI). AUCABP&lt;LLA was not significantly associated with MMOM incidence (HVx: Odds Ratio 1.000, 95% CI 0.970-1.030, p=0.979; COx: OR 1.006, 95% CI 0.990-1.023, p=0.456). However, for components of MMOM, specifically operative mortality (HVx: OR 1.063, 95% CI 1.013-1.115, p=0.013; COx: OR 1.027, 95% CI 1.001-1.052, p=0.038) and low cardiac output syndrome (HVx: OR 1.036, 95% CI 1.004-1.068, p=0.025), there were statistically significant relationships with AUCABP&lt;LLA when controlling for logistic Euroscore and preoperative hemoglobin levels. Conclusions: Area under the time-pressure curve for pressures less than patient-specific LLA was not significantly associated with MMOM incidence. However, we found significant associations between components of MMOM, specifically operative mortality and low cardiac output syndrome, with an AUCABP&lt;LLA and time under LLA. These results suggest that LLA metrics might have utility in predicting specific postoperative outcomes, but their role in predicting overall MMOM may be limited in heterogenous and high-acuity patient populations. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Abstract 13997: Identification of Novel Spectral Densities in Arterial Pressure Time Series for Cardiovascular Risk Stratification in Early Prediabetes: Machine Learning Beyond Conventional Frequency Domain Analysis

Introduction: Despite the extensive use of average BP for diagnosis of cardiovascular disease (CVD), research has shown that changes in BP variability can better reflect CV deterioration. As patients with type 2 diabetes present to the clinic with pre-established CVD, there is a pressing need for early identification of aberrant BP fluctuations useful for risk appraisal. Hypothesis: We hypothesized that deep learning models with superior capacity in feature extraction would enable comprehensive characterization of arterial pressure (AP) time-series allowing CV risk stratification in early prediabetes. Methods: We trained a convolutional neural network (CNN, Figure 1A) to classify spectrograms generated using short-term Fourier transforms of beat-to-beat AP signals collected from control vs. prediabetic rats of both sexes fed a high-calorie diet for 12 or 24 weeks, representing young and old rats. Model performance was assessed on different sampling rates for binary and multi-level classifications of sex, age, and diet. Saliency maps were generated by back-propagation of model scores to extract frequency values crucial for classification. Conventional frequency domain analysis was performed on the original AP time-series and the power spectral densities (PSD) were determined for the frequencies identified for each classification task. PSD were compared among sex, age, and diet groups using 3-way ANOVA and principal component analysis. Results: The model successfully classified AP spectrograms according to age, sex, and diet revealing sex- and age-specific CV deteriorative processes in early prediabetes (Figure 1B-E). Saliency maps identified frequencies of 2, 13, 19, and 43 Hz as crucial for DietxAgexSex classification. Comparison of PSDs for these frequencies was not sufficient for CV risk separation (Figure 1F, G). Conclusions: Employing a CNN overcomes the shortcomings associated with CV risk stratification using conventional methods.

Modeling peripheral arterial and venous pressure signals with integral pulse frequency modulation

This paper proposes a novel mathematical modeling framework for peripheral arterial and peripheral venous blood pressure signals from porcine experiments. Peripheral blood pressure signals can be acquired using regular catheters during standard patient treatment. The minimally invasive nature and ubiquitous availability of catheters render it an ideal candidate for various applications. However, there is no analytical model for peripheral blood pressure signals in the literature. We address this issue by proposing a model for these signals under the integral pulse frequency modulation (IPFM) framework. The model incorporates the impacts of physiological phenomena, such as the heartbeat pulse shape variation, heart rate variability, respiratory rate, etc. The model parameters are obtained by applying the IPFM model to experimental data collected from four pigs under different anesthetic dosages. The proposed model can fit the experimental data with Pearson correlation coefficients greater than 0.99 and 0.90 for arterial and venous blood pressure signals, respectively. The performance of model-synthesized data on the classification of two different anesthesia is comparable with experimental data. Parameters like pulse shape and duration can also work as distinguishable features under different anesthesia. We also proposed a way to distinguish respiratory-induced heart rate variability from other causes. Increasing doses of vasodilating anesthesia is similar to going from dehydration to hydration. Thus the results obtained in this study can be extended in distinguishing hydrated and dehydrated subjects. This model can be extended to similar biomedical signals like photoplethysmography, cerebral blood flow velocity, and Doppler waveforms.

Investigation of Cerebral Autoregulation Using Time-Frequency Transformations.

The authors carried out the study of the state of systemic and cerebral hemodynamics in normal conditions and in various neurosurgical pathologies using modern signal processing methods. The results characterize the condition for the mechanisms of cerebral circulation Institute of Computer Science and Control, Higher School of Cyber-Physical Systems and Control regulation, which allows for finding a solution to fundamental and specific clinical problems for the effective treatment of patients with various pathologies. The proposed method is based on the continuous wavelet transform of systemic arterial pressure and blood flow velocity signals in the middle cerebral artery recorded by non-invasive methods of photoplethysmography and transcranial doppler ultrasonography. The study of these signals in real-time in the frequency range of Mayer waves makes it possible to determine the cerebral autoregulation state in certain diseases before and after surgical interventions. The proposed method uses a cross-wavelet spectrum, which helps obtain wavelet coherence and a phase shift between the wavelet coefficients of systemic arterial pressure signals and blood flow velocity in the Mayer wave range. The obtained results enable comparing the proposed method with that based on the short-time Fourier transform. The comparison showed that the proposed method has higher sensitivity to changes in cerebral autoregulation and better localization of changes in time and frequency.

Cerebral Autoregulation Indices Are Not Interchangeable in Patients With Sepsis.

IntroductionDynamic cerebral autoregulation (dCA) is frequently altered in patients with sepsis and may be associated with sepsis-associated brain dysfunction. However, the optimal index to quantify dCA in patients with sepsis is currently unknown.ObjectiveTo assess the agreement between two validated dCA indices in patients with sepsis.MethodsRetrospective analysis of prospectively collected data in patients with sepsis; those with acute or chronic intracranial disease, arrhythmias, mechanical cardiac support, or history of supra-aortic vascular disease were excluded. Transcranial Doppler was performed on the right or left middle cerebral artery (MCA) with a 2-MHz probe, and MCA blood flow velocity (FV) and arterial pressure (BP) signals were simultaneously recorded. We calculated two indices of dCA: the mean flow index (Mxa), which is the Pearson correlation coefficient between BP and FV (MATLAB, MathWorks), and the autoregulation index (ARI), which is the transfer function analysis of spontaneous fluctuations in BP and FV (custom-written FORTRAN code). Impaired dCA was defined as Mxa >0.3 or ARI ≤ 4. The agreement between the two indices was assessed by Cohen's kappa coefficient.ResultsWe included 95 patients (age 64 ± 13 years old; male 74%); ARI was 4.38 [2.83–6.04] and Mxa was 0.32 [0.14–0.59], respectively. There was no correlation between ARI and Mxa (r = −0.08; p = 0.39). dCA was altered in 40 (42%) patients according to ARI and in 50 (53%) patients according to Mxa. ARI and Mxa were concordant in classifying 23 (24%) patients as having impaired dCA and 28 (29%) patients as having intact dCA. Cohen's kappa coefficient was 0.08, suggesting poor agreement. ARI was altered more frequently in patients on mechanical ventilation than others (27/52, 52% vs. 13/43, 30%, p = 0.04), whereas Mxa did not differ between those two groups. On the contrary, Mxa was altered more frequently in patients receiving sedatives than others (23/34, 68% vs. 27/61, 44%, p = 0.03), whereas ARI did not differ between these two groups.ConclusionsAgreement between ARI and Mxa in assessing dCA in patients with sepsis was poor. The identification of specific factors influencing the dCA analysis might lead to a better selection of the adequate cerebral autoregulation (CAR) index in critically ill patients with sepsis.

Association of Palmar Arch Collateral Function and Radial Artery Occlusion After Transradial Access

The prevalence and implications of radial artery occlusion (RAO) after transradial catheterization are an intensely discussed topic, resulting in numerous preventive strategies such as adjusted anticoagulation, residual-patency hemostasis, or distal puncture site. The present study aimed at assessing an association of palmar arch, in particular radial artery collateral function and RAO after transradial access (TRA) catheterization. Radial artery collateral function was determined using radial artery pressure signals in the nonobstructed vessel and during brief manual occlusion of the more proximal radial artery. Collateral flow index, the ratio of mean occlusive divided by mean nonocclusive arterial blood pressure, both subtracted by central venous pressure, was determined during manual RAO (radial artery collateral flow index [CFIrad]). The presence or absence of RAO was determined by Doppler ultrasound at least 3 months after TRA. A total of 630 patients with TRA coronary angiography underwent palmar arch, that is, radial and radial plus ulnar artery collateral function assessment. CFIrad was equal to 0.808 ± 0.144 (95% confidence interval 0.797 to 0.819). A total of 200 patients underwent Doppler ultrasound examination of their forearm arterial circulation 301 ± 140 days after TRA. Eight (4%) patients showed signs of RAO, 4 of whom (2%) had a complete RAO and 4 (2%) a stenosis above 30%. Patients with RAO showed a higher CFIrad than those without RAO: 0.900 ± 0.074 versus 0.801 ± 0.154 (p=0.006). In conclusion, complete RAO as determined by Doppler ultrasound later than 3 months after TRA is rare (2%). In the long run, RAO appears to be related to a very well-developed radial artery collateral function.

Deep Neural Network to Accurately Predict Left Ventricular Systolic Function Under Mechanical Assistance.

Characterizing left ventricle (LV) systolic function in the presence of an LV assist device (LVAD) is extremely challenging. We developed a framework comprising a deep neural network (DNN) and a 0D model of the cardiovascular system to predict parameters of LV systolic function. DNN input data were systemic and pulmonary arterial pressure signals, and rotation speeds of the device. Output data were parameters of LV systolic function, including end-systolic maximal elastance (Emax,lv), a variable essential for adequate hemodynamic assessment of the LV. A 0D model of the cardiovascular system, including a wide range of LVAD settings and incorporating the whole spectrum of heart failure, was used to generate data for the training procedure of the DNN. The DNN predicted Emax,lv with a mean relative error of 10.1%, and all other parameters of LV function with a mean relative error of <13%. The framework was then able to retrieve a number of LV physiological variables (i.e., pressures, volumes, and ejection fraction) with a mean relative error of <5%. Our method provides an innovative tool to assess LV hemodynamics under device assistance, which could be helpful for a better understanding of LV-LVAD interactions, and for therapeutic optimization.

Hidden Markov model-based heartbeat detector using electrocardiogram and arterial pressure signals.

The online version contains supplementary material available at 10.1007/s13534-021-00192-x.

Implementation and Calibration of a Deep Neural Network to Predict Parameters of Left Ventricular Systolic Function Based on Pulmonary and Systemic Arterial Pressure Signals.

The evaluation of cardiac contractility by the assessment of the ventricular systolic elastance function is clinically challenging and cannot be easily obtained at the bedside. In this work, we present a framework characterizing left ventricular systolic function from clinically readily available data, including systemic and pulmonary arterial pressure signals. We implemented and calibrated a deep neural network (DNN) consisting of a multi-layer perceptron with 4 fully connected hidden layers and with 16 neurons per layer, which was trained with data obtained from a lumped model of the cardiovascular system modeling different levels of cardiac function. The lumped model included a function of circulatory autoregulation from carotid baroreceptors in pulsatile conditions. Inputs for the DNN were systemic and pulmonary arterial pressure curves. Outputs from the DNN were parameters of the lumped model characterizing left ventricular systolic function, especially end-systolic elastance. The DNN adequately performed and accurately recovered the relevant hemodynamic parameters with a mean relative error of less than 2%. Therefore, our framework can easily provide complex physiological parameters of cardiac contractility, which could lead to the development of invaluable tools for the clinical evaluation of patients with severe cardiac dysfunction.

A Unified Approach for Heart Rate Estimation from Electrocardiogram and Arterial Blood Pressure Pulses

The objective of this manuscript is to propose a unique methodology for heart rate estimation derived from Electrocardiogram (ECG) or arterial blood pressure (abp) signal. This methodology relies on the identification of a signal's fundamental frequency by use of empirical wavelet analysis, followed by peak identification within windows based on pseudo-periodic assumption. The proposed methodology is based on the concept that the most of the cardiovascular signals are quasi-periodic in nature. The proposed technique estimates the fundamental frequency of the signal from its corresponding Fourier spectrum using empirical wavelet transform and then utilizes a search window for locating the peaks in the corresponding signal which identifies the R peaks in ECG or Systolic peaks in blood pressure pulses. This approach was validated on 100 recordings of the computing in cardiology challenge 2014 training data set and performance parameters were compared with methods running only on ECG or ABP signals independently.

Detection of Needle Dislodgement Using Extracorporeal Pressure Signals: A Feasibility Study.

Venous needle dislodgement (VND) during dialysis is a rarely occurring adverse event, which becomes life-threatening if not handled promptly. Because the standard venous pressure alarm, implemented in most dialysis machines, has low sensitivity, a novel approach using extracted cardiac information to detect needle dislodgement is proposed. Four features are extracted from the arterial and venous pressure signals of the dialysis machine, characterizing the mean venous pressure, the venous cardiac pulse pressure, the time delay, and the correlation between the two pressure signals. The features serve as input to a support vector machine (SVM), which determines whether dislodgement has occurred. The SVM is first trained on a set of laboratory data, and then tested on another set of laboratory data as well as on a small data set from clinical hemodialysis sessions. The results show that dislodgement can be detected after 12-17 s, corresponding to 24-143 ml blood loss. The standard venous pressure alarm used in clinical routine only detects 50% of the VNDs, whereas the novel method detects all VNDs and has a false alarm rate of 0.12 per hour, provided that the amplitude of the extracted cardiac pressure signal exceeds 1 mmHg. The results are promising; however, the method needs to be tested on a larger set of clinical data to better establish its performance.

Cardiovascular autonomic modulation and baroreflex control in the second trimester of pregnancy: A cross sectional study

PurposeThe aim is to evaluate and characterize cardiovascular autonomic control and baroreflex function and their response to an orthostatic stressor in the second trimester of pregnancy via time, frequency, information and symbolic analyses.MethodsWe evaluated 22 women at 18 weeks of pregnancy, labeled as pregnant group (PG) (30.8±4.4 years), and 22 non-pregnant women (29.8±5.4 years), labeled as control group (CG). Electrocardiogram, non-invasive photoplethysmographic arterial pressure (AP) and respiratory signals were recorded at rest at left lateral decubitus (REST) and during active standing (STAND) for 10 minutes. The heart period (HP) variability and systolic AP (SAP) variability were assessed in the frequency domain. High frequency (HF) and low frequency (LF) spectral indexes were computed. Nonlinear indexes such as symbolic markers (0V%, 1V%, 2LV% and 2UV% indexes), Shannon entropy (SE) and normalized complexity index (NCI) were calculated as well. Baroreflex control was assessed by cross-spectral HP-SAP analysis. We computed baroreflex sensitivity (BRS), HP-SAP squared coherence (K2) and phase in LF and HF bands.ResultsAt REST, the PG had lower mean, variance and HF power of HP series and lower K2(LF), BRS(LF) and BRS(HF) than the CG. During STAND, CG and PG decreased the mean, CI, NCI and 2UV% and increased 0V% of the HP series and augmented the SAP variance. LFabs of SAP series increased during STAND solely in CG. BRS(HF) was reduced during in both PG and CG, while HFabs of HP series did not diminish during STAND either in PG or CG. Complexity of the autonomic control was similar in PG and CG regardless of the experimental condition.ConclusionWe conclude that the second trimester of pregnancy was characterized by a lower parasympathetic modulation and reduced BRS at REST, preserved complexity of cardiac and vascular controls, limited sympathetic response to STAND and general conservation of the baroreflex responses to posture changes.Trial registrationBegistro Brasileiro de Ensaios clínicos, Number: RBR-9s8t88.

Short-term multiscale complexity analysis of cardiovascular variability improves low cardiac output syndrome risk stratification after coronary artery bypass grafting

Background: Low cardiac output syndrome (LCOS) is a myocardial dysfunction leading to systemic hypoperfusion, favored by particular conditions of the autonomic nervous system. LCOS is one of the adverse events that might occur after cardiac surgery. Objective: The aim is to test the hypothesis that short-term multiscale complexity (MSC) analysis of heart period (HP) and systolic arterial pressure (SAP) variability series in the frequency bands typical of cardiovascular control could be fruitfully exploited in identifying subjects at risk of developing LCOS after coronary artery bypass graft (CABG). Approach: HP and SAP beat-to-beat series were derived from electrocardiogram (ECG) and invasive arterial pressure (AP) signal acquired in 128 patients scheduled for CABG before (PRE) and after (POST) the induction of general anesthesia with propofol and remifentanil. Subjects were labeled as LCOS (n = 14) and noLCOS (n = 114) according to the LCOS development. MSC markers were calculated as the complement to 1 of the modulus of the average position of the poles dropping in the low-frequency (LF, 0.04–0.15 Hz) and high-frequency (HF, 0.15–0.5 Hz) bands as derived from the autoregressive model of HP and SAP series. Traditional time and frequency domain indexes were also calculated. Main results: Traditional parameters were able to assess the depression of the cardiovascular regulation induced by general anesthesia, but showed weak performances in differentiating LCOS and noLCOS groups. Conversely, HP complexity in LF band and SAP complexity in HF band assessed during POST remained associated with LCOS even after entering a multivariate logistic regression model adjusted for clinical and demographic factors. Significance: The MSC approach can be fruitfully applied to improve risk stratification for LCOS after CABG likely because MSC markers describe the dysfunction of the sympathetic control and the impairment of the mechanical properties of the heart in the LCOS group.

The effect of caffeine loading on cerebral autoregulation in preterm infants.

To evaluate cerebral autoregulation changes in preterm infants receiving a loading dose of caffeine base. In a cohort of 30 preterm infants, we extracted measures of cerebral autoregulation using time and frequency domain techniques to determine the correlation between mean arterial pressure (MAP) and tissue oxygenation index (TOI) signals. These measures included the cerebral oximetry index (COx), cross-correlation and coherence measures, and were extracted prior to caffeine loading and in the 2hours following administration of 10mg/kg caffeine base. We observed acute reductions in time domain correlation measures, including the cerebral oximetry index (linear mixed model coefficient -0.093, standard error 0.04; p=0.028) and the detrended cross-correlation coefficient (ρ5 coefficient -0.13, standard error 0.055; p=0.025). These reductions suggested an acute improvement in cerebral autoregulation. Features from detrended cross-correlation analysis also showed greater discriminative value than other methods in identifying changes prior to and following caffeine administration. We observed a reduced correlation between MAP and TOI from near-infrared spectroscopy following caffeine administration. These findings suggest an acute enhanced capacity for cerebral autoregulation following a loading dose of caffeine in preterm infants, contributing to our understanding of the physiological impact of caffeine therapy.

Towards understanding the complexity of cardiovascular oscillations: Insights from information theory

Cardiovascular complexity is a feature of healthy physiological regulation, which stems from the simultaneous activity of several cardiovascular reflexes and other non-reflex physiological mechanisms. It is manifested in the rich dynamics characterizing the spontaneous heart rate and blood pressure variability (HRV and BPV).The present study faces the challenge of disclosing the origin of short-term HRV and BPV from the statistical perspective offered by information theory. To dissect the physiological mechanisms giving rise to cardiovascular complexity in different conditions, measures of predictive information, information storage, information transfer and information modification were applied to the beat-to-beat variability of heart period (HP), systolic arterial pressure (SAP) and respiratory volume signal recorded non-invasively in 61 healthy young subjects at supine rest and during head-up tilt (HUT) and mental arithmetics (MA). Information decomposition enabled to assess simultaneously several expected and newly inferred physiological phenomena, including: (i) the decreased complexity of HP during HUT and the increased complexity of SAP during MA; (ii) the suppressed cardiorespiratory information transfer, related to weakened respiratory sinus arrhythmia, under both challenges; (iii) the altered balance of the information transferred along the two arms of the cardiovascular loop during HUT, with larger baroreflex involvement and smaller feedforward mechanical effects; and (iv) an increased importance of direct respiratory effects on SAP during HUT, and on both HP and SAP during MA. We demonstrate that a decomposition of the information contained in cardiovascular oscillations can reveal subtle changes in system dynamics and improve our understanding of the complexity changes during physiological challenges.

Alterations in the coupling functions between cerebral oxyhaemoglobin and arterial blood pressure signals in post-stroke subjects.

Cerebral autoregulation (CA) is the complex homeostatic regulatory relationship between blood pressure (BP) and cerebral blood flow (CBF). This study aimed to analyze the frequency-specific coupling function between cerebral oxyhemoglobin concentrations (delta [HbO2]) and mean arterial pressure (MAP) signals based on a model of coupled phase oscillators and dynamical Bayesian inference. Delta [HbO2] was measured by 24-channel near-infrared spectroscopy (NIRS) and arterial BP signals were obtained by simultaneous resting-state measurements in patients with stroke, that is, 9 with left hemiparesis (L–H group), 8 with right hemiparesis (R–H group), and 17 age-matched healthy individuals as control (healthy group). The coupling functions from MAP to delta [HbO2] oscillators were identified and analyzed in four frequency intervals (I, 0.6–2 Hz; II, 0.145–0.6 Hz; III, 0.052–0.145 Hz; and IV, 0.021–0.052 Hz). In L–H group, the CS from MAP to delta [HbO2] in interval III in channel 8 was significantly higher than that in healthy group (p = 0.003). Compared with the healthy controls, the coupling in MAP→delta [HbO2] showed higher amplitude in interval I and IV in patients with stroke. The increased CS and coupling amplitude may be an evidence of impairment in CA, thereby confirming the presence of impaired CA in patients with stroke. In interval III, the CS in L–H group from MAP to delta [HbO2] in channel 16 (p = 0.001) was significantly lower than that in healthy controls, which might indicate the compensatory mechanism in CA of the unaffected side in patients with stroke. No significant difference in region-wise CS between affected and unaffected sides was observed in stroke groups, indicating an evidence of globally impaired CA. These findings provide a method for the assessment of CA and will contribute to the development of therapeutic interventions in stroke patients.

Development of a Non-invasive Cerebrovascular Status Algorithm to Estimate Cerebral Perfusion Pressure and Intracranial Pressure in a Porcine Model of Focal Brain Injury.

New tools for diagnosis, monitoring, and treatment of elevated intracranial pressure (ICP) or compromised cerebral perfusion pressure (CPP) are urgently needed to improve outcomes after brain injury. Previous success in applying advanced data analytics to build precision monitors based on large, noisy sensor datasets suggested applying the same approach to monitor cerebrovascular status. In these experiments, a new algorithm was developed to estimate ICP and CPP using the arterial pressure waveform. Sixty-five porcine subjects were subjected to a focal brain injury to simulate a mass lesion with elevated ICP. The arterial pressure waveform and the measured ICP from these subjects during injury and treatment were then utilized to develop and calibrate an ICP and CPP estimation algorithm. These estimation algorithms were then subsequently evaluated on 14 new subjects. The root mean square difference between actual ICP and estimated ICP was 2.0961 mmHg. The root mean square difference between the actual CPP and the estimated CPP was 2.6828 mmHg. A novel ICP or CPP monitor based on the arterial pressure signal produced a very close approximation to actual measured ICP and CPP and warrants further evaluation.

Self-Powered Real-Time Arterial Pulse Monitoring Using Ultrathin Epidermal Piezoelectric Sensors.

Continuous monitoring of an arterial pulse using a pressure sensor attached on the epidermis is an important technology for detecting the early onset of cardiovascular disease and assessing personal health status. Conventional pulse sensors have the capability of detecting human biosignals, but have significant drawbacks of power consumption issues that limit sustainable operation of wearable medical devices. Here, a self-powered piezoelectric pulse sensor is demonstrated to enable in vivo measurement of radial/carotid pulse signals in near-surface arteries. The inorganic piezoelectric sensor on an ultrathin plastic achieves conformal contact with the complex texture of the rugged skin, which allows to respond to the tiny pulse changes arising on the surface of epidermis. Experimental studies provide characteristics of the sensor with a sensitivity (≈0.018 kPa-1 ), response time (≈60 ms), and good mechanical stability. Wireless transmission of detected arterial pressure signals to a smart phone demonstrates the possibility of self-powered and real-time pulse monitoring system.