Search results for "Arterial pressure variability"

showing 5 items of 5 documents

Information domain approach to the investigation of cardio-vascular, cardio-pulmonary, and vasculo-pulmonary causal couplings

2011

The physiological mechanisms related to cardio-vascular (CV), cardio-pulmonary (CP), and vasculo-pulmonary (VP) regulation may be probed through multivariate time series analysis tools. This study applied an information domain approach for the evaluation of non-linear causality to the beat-to-beat variability series of heart period (t), systolic arterial pressure (s), and respiration (r) measured during tilt testing and paced breathing (PB) protocols. The approach quantifies the causal coupling from the series i to the series j (C(ij)) as the amount of information flowing from i to j. A measure of directionality is also obtained as the difference between two reciprocal causal couplings (D(i…

medicine.medical_specialtySupine positioncausalityPhysiologySpeech recognitionBaroreflexlcsh:Physiologypaced breathingconditional entropyhead-up tiltInternal medicinePhysiology (medical)medicineHeart rate variabilitybaroreflexarterial pressure variabilityrespiratory sinus arrhythmiaVagal toneRespiratory systemOriginal Researchlcsh:QP1-981business.industryheart rate variabilityCardiorespiratory fitnessBlood pressureSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaCardiologyBreathingArterial pressure variability; Baroreflex; Causality; Conditional entropy; Head-up tilt; Heart rate variability; Paced breathing; Respiratory sinus arrhythmia; Physiology; Physiology (medical)business
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Assessing Causality in normal and impaired short-term cardiovascular regulation via nonlinear prediction methods

2009

We investigated the ability of mutual nonlinear prediction methods to assess causal interactions in short-term cardiovascular variability during normal and impaired conditions. Directional interactions between heart period (RR interval of the ECG) and systolic arterial pressure (SAP) short-term variability series were quantified as the cross-predictability (CP) of one series given the other, and as the predictability improvement (PI) yielded by the inclusion of samples of one series into the prediction of the other series. Nonlinear prediction was performed through global approximation (GA), approximation with locally constant models (LA0) and approximation with locally linear models (LA1) …

Adultmedicine.medical_specialtySupine positionTime FactorsGeneral MathematicsRR intervalGlobal nonlinear predictionGeneral Physics and AstronomyNeurally-mediated syncopeBlood PressureK-nearest neighbours local nonlinear predictionCardiovascular SystemSyncopeCardiovascular Physiological PhenomenaPhysics and Astronomy (all)Engineering (all)Control theoryHeart RateNeurally mediated syncopeInternal medicinemedicinePressureHumansMathematics (all)Computer SimulationOut-of-sample predictionMathematicsModels StatisticalGeneral EngineeringLinear modelModels CardiovascularNonlinear granger causalityModels TheoreticalControl subjectsHeart rate and arterial pressure variabilityCausalityNonlinear predictionTerm (time)Case-Control StudiesSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaCardiologyAlgorithms
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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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Cardiovascular control and time domain granger causality: Insights from selective autonomic blockade

2013

We studied causal relations among heart period (HP), systolic arterial pressure (SAP) and respiration (R) according to the definition of Granger causality in the time domain. Autonomic pharmacological challenges were used to alter the complexity of cardiovascular control. Atropine (AT), propranolol and clonidine (CL) were administered to block muscarinic receptors, β-adrenergic receptors and centrally sympathetic outflow, respectively. We found that: (i) at baseline, HP and SAP interacted in a closed loop with a dominant causal direction from HP to SAP; (ii) pharmacological blockades did not alter the bidirectional closed-loop interactions between HP and SAP, but AT reduced the dominance of…

AdultMaleGeneral MathematicsGeneral Physics and AstronomyBlood PressurePropranololPharmacologyBaroreflexArterial pressure variability; Autonomic nervous system; Baroreflex; Cardiovascular control; Granger causality; Heart rate variability; Mathematics (all); Engineering (all); Physics and Astronomy (all)Models BiologicalPhysics and Astronomy (all)Engineering (all)Respiratory RateGranger causalityBiological ClocksHeart RateMuscarinic acetylcholine receptormedicineHumansHeart rate variabilityAutonomic nervous systemMathematics (all)Computer SimulationHeart rate variabilityFeedback PhysiologicalChemistryGeneral EngineeringMiddle AgedBaroreflexClonidineAtropineAutonomic nervous systemCardiovascular controlSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityFemaleArterial pressure variabilityAutonomic Nerve Blockmedicine.drug
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Assessment of Granger causality by nonlinear model identification: application to short-term cardiovascular variability.

2007

A method for assessing Granger causal relationships in bivariate time series, based on nonlinear autoregressive (NAR) and nonlinear autoregressive exogenous (NARX) models is presented. The method evaluates bilateral interactions between two time series by quantifying the predictability improvement (PI) of the output time series when the dynamics associated with the input time series are included, i.e., moving from NAR to NARX prediction. The NARX model identification was performed by the optimal parameter search (OPS) algorithm, and its results were compared to the least-squares method to determine the most appropriate method to be used for experimental data. The statistical significance of…

Biomedical EngineeringBlood PressureBivariate analysisDirectionalitySensitivity and SpecificitySurrogate dataFeedbackNonlinear parametric modelGranger causalityControl theoryHeart RateOptimal parameter searchStatisticsAnimalsHumansComputer SimulationPredictabilityHeart rate variabilityMathematicsNonlinear autoregressive exogenous modelCardiovascular regulationSystem identificationModels CardiovascularNonlinear systemAutoregressive modelNonlinear DynamicsAutoregressive exogenous modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaRegression AnalysisSurrogate dataArterial pressure variabilityAlgorithmsAnnals of biomedical engineering
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