Search results for "Cardiovascular variability"

showing 10 items of 13 documents

Measuring postural-related changes of spontaneous baroreflex sensitivity after repeated long-duration diving: Frequency domain approaches

2012

Sustained water immersion is thought to modulate orthostatic tolerance to an extent dependent on the duration and repetition over consecutive days of the diving sessions. We tested this hypothesis investigating in ten healthy subjects the potential changes in the cardiovascular response to head-up tilt induced by single and multiple resting air dives. Parametric cross-spectral analysis of spontaneous RR interval and systolic arterial pressure variability was performed in three experimental sessions: before diving (BD), after single 6-hour dive (ASD), and after multiple 6-hour dives (AMD, 5 consecutive days with 18-hour surface interval). From this analysis, baroreflex sensitivity (BRS) was …

AdultMalemedicine.medical_specialtySupine positionDivingPostureRR intervalOrthostatic intoleranceBaroreflexSensitivity and SpecificityEndocrine and Autonomic SystemCellular and Molecular NeuroscienceOrthostatic vital signsInternal medicinemedicineHumansShort durationAnalysis of VarianceElectronic Data ProcessingEndocrine and Autonomic Systemsbusiness.industrySpectrum AnalysisHead-up tiltBaroreflexCardiovascular variabilitymedicine.diseaseCausal coherenceParametric cross-spectral analysiFrequency domainSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaPower ratioCardiologyNeurology (clinical)businessOrthostatic toleranceAutonomic Neuroscience
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Testing Frequency-Domain Causality in Multivariate Time Series

2010

We introduce a new hypothesis-testing framework, based on surrogate data generation, to assess in the frequency domain, the concept of causality among multivariate (MV) time series. The approach extends the traditional Fourier transform (FT) method for generating surrogate data in a MV process and adapts it to the specific issue of causality. It generates causal FT (CFT) surrogates with FT modulus taken from the original series, and FT phase taken from a set of series with causal interactions set to zero over the direction of interest and preserved over all other directions. Two different zero-setting procedures, acting on the parameters of a MV autoregressive (MVAR) model fitted on the ori…

AdultMultivariate statisticsTime FactorsBiomedical EngineeringSurrogate datasymbols.namesakemultivariate autoregressive (MVAR) modeldirected coherence (DC)StatisticsHumansCoherence (signal processing)Computer SimulationEEGMathematicsSignal processingsurrogate dataFourier Analysispartial directed coherence (PDC)Models CardiovascularReproducibility of ResultsEstimatorElectroencephalographySignal Processing Computer-AssistedCardiovascular variabilityFourier transformAutoregressive modelFrequency domainMultivariate AnalysisSettore ING-INF/06 - Bioingegneria Elettronica E InformaticasymbolsAlgorithmAlgorithmsIEEE Transactions on Biomedical Engineering
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Mixed predictability and cross-validation to assess non-linear Granger causality in short cardiovascular variability series

2006

A method to evaluate the direction and strength of causal interactions in bivariate cardiovascular and cardiorespiratory series is presented. The method is based on quantifying self and mixed predictability of the two series using nearest-neighbour local linear approximation. It returns two causal coupling indexes measuring the relative improvement in predictability along direct and reverse directions, and a directionality index indicating the preferential direction of interaction. The method was implemented through a cross-validation approach that allowed quantification of directionality without constraining the embedding of the series, and fully exploited the available data to maximise th…

AdultStatistics as TopicBiomedical EngineeringInferenceBlood PressureHealth InformaticsBivariate analysisDirectionalityCross-validationGranger causalityHeart RateStatisticsEconometricsHumansComputer SimulationPredictabilityMathematicsSeries (mathematics)Models CardiovascularNonlinear systemNonlinear DynamicsData Interpretation StatisticalShort-term cardiovascular variabilityRespiratory MechanicsRegression AnalysisFemaleNon-linear predictionLinear approximationAlgorithmsBiomedizinische Technik/Biomedical Engineering
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Univariate and multivariate conditional entropy measures for the characterization of short-term cardiovascular complexity under physiological stress

2017

Objective: A defining feature of physiological systems under the neuroautonomic regulation is their dynamical complexity. The most common approach to assess physiological complexity from short-term recordings, i.e. to compute the rate of entropy generation of an individual system by means of measures of conditional entropy (CE), does not consider that complexity may change when the investigated system is part of a network of physiological interactions. This study aims at extending the concept of short-term complexity towards the perspective of network physiology, defining multivariate CE measures whereby multiple physiological processes are accounted for in the computation of entropy rates.…

MaleMultivariate statisticsAdolescentPhysiologyEntropyBiomedical EngineeringBiophysicsDiastoleBlood Pressure030204 cardiovascular system & hematologynetwork physiologyCardiovascular Physiological PhenomenaEntropy estimation03 medical and health sciences0302 clinical medicinehead-up tiltHeart RateStress PhysiologicalPhysiology (medical)StatisticsHumansVagal toneMathematicsConditional entropymental streResting state fMRIRespirationModels CardiovascularUnivariateBlood pressureBiophysicSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaMultivariate AnalysisFemalecardiovascular variabilitycomplexity030217 neurology & neurosurgeryPhysiological Measurement
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Extended causal modeling to assess Partial Directed Coherence in multiple time series with significant instantaneous interactions.

2010

The Partial Directed Coherence (PDC) and its generalized formulation (gPDC) are popular tools for investigating, in the frequency domain, the concept of Granger causality among multivariate (MV) time series. PDC and gPDC are formalized in terms of the coefficients of an MV autoregressive (MVAR) model which describes only the lagged effects among the time series and forsakes instantaneous effects. However, instantaneous effects are known to affect linear parametric modeling, and are likely to occur in experimental time series. In this study, we investigate the impact on the assessment of frequency domain causality of excluding instantaneous effects from the model underlying PDC evaluation. M…

Multivariate statisticsTime FactorsGeneral Computer ScienceModels NeurologicalPattern Recognition AutomatedCardiovascular Physiological PhenomenaElectrocardiographyGranger causalityArtificial IntelligenceEconometricsCoherence (signal processing)AnimalsHumansComputer SimulationEEGPartial Directed CoherenceMathematicsCausal modelMultivariate autoregressive modelComputer Science (all)Linear modelElectroencephalographySignal Processing Computer-AssistedCardiovascular variabilityAutoregressive modelFrequency domainParametric modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityMultivariate time serieLinear ModelsNeural Networks ComputerBiotechnologyBiological cybernetics
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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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Surrogate Data Analysis for Assessing the Significance of the Coherence Function

2004

In cardiovascular variability analysis, the significance of the coupling between two time series is commonly assessed by setting a threshold level in the coherence function. While traditionally used statistical tests consider only the parameters of the adopted estimator, the required zero-coherence level may be affected by some features of the observed series. In this study, three procedures, based on the generation of surrogate series sharing given properties with the original but being structurally uncoupled, were considered: independent identically distributed (IID), Fourier transform (FT), and autoregressive (AR). IID surrogates maintained the distribution of the original series, while …

Myocardial InfarctionBiomedical EngineeringBlood PressureSurrogate dataSpectral analysisymbols.namesakeHeart RateStatisticsCoherence functionHumansCoherence (signal processing)Computer SimulationStatistical physicsCoupling significanceSpurious relationshipMathematicsStatistical hypothesis testingRespirationModels CardiovascularSpectral densityEstimatorCardiovascular variabilityFourier transformAutoregressive modelData Interpretation StatisticalsymbolsRegression AnalysisSurrogate dataAlgorithmsIEEE Transactions on Biomedical Engineering
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An Empirical Mode Decomposition Approach to Assess the Strength of Heart Period-Systolic Arterial Pressure Variability Interactions.

2020

This work proposes an empirical mode decomposition (EMD) method to assess the strength of the interactions between heart period (HP) and systolic arterial pressure (SAP) variability. EMD was exploited to decompose the original series (OR) into its first, and fastest, intrinsic mode function (IMF1) and the residual (RES) computed by subtracting the IMF1 from OR. EMD procedure was applied to both HP and SAP variability series. Then, the cross correlation function (CCF) was computed over OR, IMF1 and RES series derived from HP and SAP variability in 13 healthy subjects (age 27±8 yrs, 5 males) at rest in supine position (REST) and during head-up tilt (TILT). The first CCF maximum at negative ti…

Rest (physics)MaleSupine positionMathematical analysisWork (physics)Blood PressureHeart030204 cardiovascular system & hematologyBaroreflexBaroreflexResidualCardiovascular variabilityHilbert–Huang transform03 medical and health sciences0302 clinical medicineTilt (optics)Heart RateRespirationSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaArterial Pressure030217 neurology & neurosurgeryMathematicsAnnual 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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Experimental approach for testing the uncoupling between cardiovascular variability series

2002

In cardiovascular variability analysis, the significance of the coupling between two series is commonly assessed by defining a zero level on the magnitude-squared coherence (MSC). Although the use of the conventional value of 0.5 does not consider the dependence of MSC estimates on the analysis parameters, a theoretical threshold Tt is available only for the weighted covariance (WC) estimator. In this study, an experimental threshold for zero coherence Te was derived by a statistical test from the sampling distribution of MSC estimated on completely uncoupled time series. MSC was estimated by the WC method (Parzen window, spectral bandwidth B = 0.015, 0.02, 0.025, 0.03 Hz) and by the parame…

Series (mathematics)Kernel density estimationModels CardiovascularMyocardial InfarctionBiomedical EngineeringEstimatorComputer Science Applications1707 Computer Vision and Pattern RecognitionSignal Processing Computer-AssistedCoherence (statistics)CovarianceFeedbackComputer Science ApplicationsSpectral analysiElectrocardiographySampling distributionAutoregressive modelCardiovascular variability serieStatisticsHumansMagnitude-squared coherenceParametric statisticsMathematicsMedical & Biological Engineering & Computing
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Input for baroreflex analysis: which blood pressure signal should be used?

2022

The baroreflex (BR) is an important physiological regulatory mechanism which reacts to blood pressure perturbations with reflex changes of target variables such as the heart period (electrocardiogram derived RR interval) or the peripheral vascular resistance (PVR). Evaluation of cardiac chronotropic (RR as a target variable) and vascular resistance (target PVR) BR arms was in previous studies mainly based on the use of the spontaneous variability of the systolic or diastolic blood pressure (SBP, DBP), respectively, as the input signals. The use of other blood pressure measures such as the mean blood pressure (MBP) as an input signal for BR analysis is still under investigation. Making the a…

Settore ING-INF/06 - Bioingegneria Elettronica E InformaticaArterial baroreflex blood pressure cardiovascular variability spectral analysis transfer function head-up tilt mental arithmetic task muscle sympathetic nerve activity
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