Search results for "Systolic arterial pressure"

showing 10 items of 14 documents

Redundant and synergistic information transfer in cardiovascular and cardiorespiratory variability

2015

In the framework of information dynamics, new tools are emerging which allow one to quantify how the information provided by two source processes about a target process results from the contribution of each source and from the interaction between the sources. We present the first implementation of these tools in the assessment of short-term cardiovascular and cardiorespiratory variability, by introducing two strategies for the decomposition of the information transferred to heart period (HP) variability from systolic arterial pressure (SAP) and respiration flow (RF) variability. Several measures based on the notion of transfer entropy (TE) are defined to quantify joint, individual and redun…

AdultMaleInformation transferComputer scienceEntropyBiomedical EngineeringBlood PressureHealth Informaticscomputer.software_genreCardiovascular Physiological PhenomenaElectrocardiographyHeart RateHumansPaced breathingSimulation1707Motor NeuronsRespirationModels CardiovascularHealthy subjectsHeartCardiorespiratory fitnessHealthy VolunteersSignal ProcessingSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaSystolic arterial pressureFemaleTransfer entropyData miningcomputer
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Exploring directionality in spontaneous heart period and systolic pressure variability interactions in humans: implications in the evaluation of baro…

2004

Although in physiological conditions RR interval and systolic arterial pressure (SAP) are likely to interact in a closed loop, the traditional cross-spectral analysis cannot distinguish feedback (FB) from feedforward (FF) influences. In this study, a causal approach was applied for calculating the coherence from SAP to RR ( Ks-r) and from RR to SAP ( Kr-s) and the gain and phase of the baroreflex transfer function. The method was applied, compared with the noncausal one, to RR and SAP series taken from 15 healthy young subjects in the supine position and after passive head-up tilt. For the low frequency (0.04–0.15 Hz) spectral component, the enhanced FF coupling ( Kr-s = 0.59 ± 0.21, signi…

AdultMalemedicine.medical_specialtySympathetic Nervous SystemPhysiologyPeriod (gene)PostureRR intervalBlood PressureBaroreflexHeart RateTilt-Table TestCoherence and transfer functionFeedback and feedforward mechanismPhysiology (medical)Internal medicineHumansMedicineDirectionalityNonbaroreflex interactionFeedback Physiologicalbusiness.industryModels CardiovascularCardiovascular regulationHeartVagus NerveBaroreflexBlood pressureCirculatory systemCardiologySystolic arterial pressureFemaleCross-spectral analysiCardiology and Cardiovascular MedicinebusinessClosed loopAmerican Journal of Physiology-Heart and Circulatory Physiology
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Evidence of unbalanced regulatory mechanism of heart rate and systolic pressure after acute myocardial infarction

2002

The interactions between systolic arterial pressure (SAP) and R-R interval (RR) fluctuations after acute myocardial infarction (AMI) were investigated by measures of synchronization separating the feedback from the feedforward control and capturing both linear and nonlinear contributions. The causal synchronization, evaluating the ability of RR to predict SAP (χs/t) or vice versa (χt/s), and the global synchronization (χ) were estimated at rest and after head-up tilt in 35 post-AMI patients, 20 young and 12 old. Significance and nonlinearity of the coupling were assessed by surrogate data analysis. Tilting increased the number of young subjects in which RR-SAP link was significant (from 17…

Adultmedicine.medical_specialtyPhysiologyMyocardial InfarctionHemodynamicsBlood PressureSynchronizationAutonomic Nervous SystemHeart RatePhysiology (medical)Internal medicineHeart ratemedicineNonlinear couplingHumansMyocardial infarctionNonlinear couplingAgedFeedback PhysiologicalSurrogate data analysisbusiness.industryCausal analysicausal analysis; nonlinear coupling; synchronization; baroreflex regulationcausal analysisBaroreflexMiddle Agedmedicine.diseaseEndocrinologyBlood pressureNeural regulationSystolic arterial pressureCardiologyBaroreflex regulationCardiology and Cardiovascular Medicinebusiness
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Bivariate nonlinear prediction to quantify the strength of complex dynamical interactions in short-term cardiovascular variability.

2005

A nonlinear prediction method for investigating the dynamic interdependence between short length time series is presented. The method is a generalization to bivariate prediction of the univariate approach based on nearest neighbor local linear approximation. Given the input and output series x and y, the relationship between a pattern of samples of x and a synchronous sample of y was approximated with a linear polynomial whose coefficients were estimated from an equation system including the nearest neighbor patterns in x and the corresponding samples in y. To avoid overfitting and waste of data, the training and testing stages of the prediction were designed through a specific out-of-sampl…

Bivariate time seriePhysics::Medical PhysicsBiomedical EngineeringBlood PressureBivariate analysisOverfittingCross-validationk-nearest neighbors algorithmCardiovascular Physiological PhenomenaHealth Information ManagementHeart RateTilt-Table TestStatisticsApplied mathematicsHumansComputer SimulationPredictabilityHeart rate variabilityMathematicsHealth InformaticBaroreflex controlSystolic arterial pressure variabilityUnivariateModels CardiovascularNonlinear predictionComputer Science Applications1707 Computer Vision and Pattern RecognitionComputer Science ApplicationsNonlinear systemComputational Theory and MathematicsNonlinear DynamicsLinear approximationMedicalbiological engineeringcomputing
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Feasibility of Linear Parametric Estimation of Dynamic Information Measures to assess Physiological Stress from Short-Term Cardiovascular Variability

2021

Extensive efforts have been recently devoted to implement fast and reliable algorithms capable of assessing the physiological response of the organism to physiological stress. In this study, we propose the comparison between model-free and linear parametric methods as regards their ability to detect alterations in the dynamics and in the complexity of cardiovascular and respiratory variability evoked by postural and mental stress. Dynamic entropy (DE) and information storage (IS) measures were calculated on three physiological time-series, i.e. heart period, respiratory volume and systolic arterial pressure, on 61 healthy subjects monitored in resting conditions as well as during head-up ti…

Dynamic entropylinear parametric estimationHeartCardiovascular Systeminformation storagesystolic arterial pressureHeart RatePregnancyStress PhysiologicalSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaFeasibility StudiesHumansFemaleheart rate variability (HRV)Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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A new Frequency Domain Measure of Causality based on Partial Spectral Decomposition of Autoregressive Processes and its Application to Cardiovascular…

2019

We present a new method to quantify in the frequency domain the strength of directed interactions between linear stochastic processes. This issue is traditionally addressed by the directed coherence (DC), a popular causality measure derived from the spectral representation of vector autoregressive (AR) processes. Here, to overcome intrinsic limitations of the DC when it needs to be objectively quantified within specific frequency bands, we propose an approach based on spectral decomposition, which allows to isolate oscillatory components related to the pole representation of the vector AR process in the Z-domain. Relating the causal and non-causal power content of these components we obtain…

Frequency band0206 medical engineering02 engineering and technologyTransfer functionRadio spectrumMatrix decomposition03 medical and health sciences0302 clinical medicineheart rateHumansCoherence (signal processing)Arterial PressureMathematicsStochastic Processespole-specific spectral causality (PSSC)Stochastic processHeartsystolic arterial pressure (SAP)Baroreflex020601 biomedical engineeringCausalityAutoregressive modelFrequency domainautoregressive processeSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaAlgorithmdirected coherence030217 neurology & neurosurgery
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Multivariate and Multiscale Complexity of Long-Range Correlated Cardiovascular and Respiratory Variability Series

2020

Assessing the dynamical complexity of biological time series represents an important topic with potential applications ranging from the characterization of physiological states and pathological conditions to the calculation of diagnostic parameters. In particular, cardiovascular time series exhibit a variability produced by different physiological control mechanisms coupled with each other, which take into account several variables and operate across multiple time scales that result in the coexistence of short term dynamics and long-range correlations. The most widely employed technique to evaluate the dynamical complexity of a time series at different time scales, the so-called multiscale …

Multivariate statisticsSystolic arterial pressure (SAP)Vector autoregressive fractionally integrated (VARFI) modelsComputer scienceGeneral Physics and Astronomylcsh:Astrophysics01 natural sciencesArticle010305 fluids & plasmaslcsh:QB460-4660103 physical sciencesRange (statistics)Multi-scale entropy (MSE)lcsh:Science010306 general physicsRepresentation (mathematics)Parametric statisticsvector autoregressive fractionally integrated (VARFI) modelSeries (mathematics)multi-scale entropy (MSE)Stochastic processsystolic arterial pressure (SAP)lcsh:QC1-999Term (time)Autoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E Informaticavector autoregressive fractionally integrated (VARFI) modelslcsh:QBiological systemHeart rate variability (HRV)lcsh:Physicsheart rate variability (HRV)
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Vector Autoregressive Fractionally Integrated Models to Assess Multiscale Complexity in Cardiovascular and Respiratory Time Series

2020

Cardiovascular variability is the result of the activity of several physiological control mechanisms, which involve different variables and operate across multiple time scales encompassing short term dynamics and long range correlations. This study presents a new approach to assess the multiscale complexity of multivariate time series, based on linear parametric models incorporating autoregressive coefficients and fractional integration. The approach extends to the multivariate case recent works introducing a linear parametric representation of multiscale entropy, and is exploited to assess the complexity of cardiovascular and respiratory time series in healthy subjects studied during postu…

Multivariate statisticsvector autoregressive fractionally integrated (VARFI) modelComputer scienceQuantitative Biology::Tissues and OrgansPhysics::Medical Physicssystolic arterial pressure (SAP)Cardiovascular variabilitycomputer.software_genreCorrelationAutoregressive modelmultiscale entropy (MSE)heart period (HP)Settore ING-INF/06 - Bioingegneria Elettronica E InformaticaParametric modelMultiple timeEntropy (information theory)Data miningTime seriescomputerParametric statistics2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)
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Disentangling cardiovascular control mechanisms during head-down tilt via joint transfer entropy and self-entropy decompositions

2015

A full decomposition of the predictive entropy (PE) of the spontaneous variations of the heart period (HP) given systolic arterial pressure (SAP) and respiration (R) is proposed. The PE of HP is decomposed into the joint transfer entropy (JTE) from SAP and R to HP and self-entropy (SE) of HP. The SE is the sum of three terms quantifying the synergistic/redundant contributions of HP and SAP, when taken individually and jointly, to SE and one term conditioned on HP and SAP denoted as the conditional SE (CSE) of HP given SAP and R. The JTE from SAP and R to HP is the sum of two terms attributable to SAP or R plus an extra term describing the redundant/synergistic contribution to the JTE. All q…

Supine positionInformation storageComputer sciencePhysiologyAutonomic nervous system; Baroreflex; Blood pressure variability; Cardiopulmonary coupling; Heart rate variability; Information dynamics; Multivariate linear regression analysis; Physiology; Physiology (medical)Cardiovascular controlAutonomic Nervous Systemlcsh:PhysiologyNuclear magnetic resonanceCardiopulmonary couplingPhysiology (medical)Cardiac controlHeart rate variabilityOriginal Researchlcsh:QP1-981redundancymultivariate linear regression analysiscardiopulmonary couplingBaroreflexHead-Down TiltInformation dynamicSynergySettore ING-INF/06 - Bioingegneria Elettronica E InformaticaSystolic arterial pressureTransfer entropyblood pressure variabilityMultivariate linear regression analysiinformation dynamicsAlgorithmFrontiers in Physiology
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Feasibility of Ultra-Short-Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and during Stress via Time-Domain and Entro…

2022

Heart Rate Variability (HRV) and Blood Pressure Variability (BPV) are widely employed tools for characterizing the complex behavior of cardiovascular dynamics. Usually, HRV and BPV analyses are carried out through short-term (ST) measurements, which exploit ~five-minute-long recordings. Recent research efforts are focused on reducing the time series length, assessing whether and to what extent Ultra-Short-Term (UST) analysis is capable of extracting information about cardiovascular variability from very short recordings. In this work, we compare ST and UST measures computed on electrocardiographic R-R intervals and systolic arterial pressure time series obtained at rest and during both post…

electrocardiography (ECG)Short-Term (ST) cardiovascular variabilityBlood PressureHeart Rate Variability (HRV)Settore ING-INF/01 - ElettronicaBiochemistryAtomic and Molecular Physics and OpticsHeart Rate Variability (HRV); Short-Term (ST) cardiovascular variability; Ultra-Short-Term (UST) HRV; electrocardiography (ECG); Systolic Arterial Pressure (SAP); entropy; conditional entropy; complexity; time-series analysisUltra-Short- Term (UST) HRVAnalytical Chemistryconditional entropyElectrocardiographyHeart RateSettore ING-INF/06 - Bioingegneria Elettronica E Informaticatime-series analysisArterial PressureElectrical and Electronic EngineeringentropycomplexitySystolic Arterial Pressure (SAP)InstrumentationSensors; Volume 22; Issue 23; Pages: 9149
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