Search results for "causa."

showing 10 items of 647 documents

Measuring Connectivity in Linear Multivariate Processes: Definitions, Interpretation, and Practical Analysis

2011

This tutorial paper introduces a common framework for the evaluation of widely used frequency-domain measures of coupling (coherence, partial coherence) and causality (directed coherence, partial directed coherence) from the parametric representation of linear multivariate (MV) processes. After providing a comprehensive time-domain definition of the various forms of connectivity observed in MV processes, we particularize them to MV autoregressive (MVAR) processes and derive the corresponding frequency-domain measures. Then, we discuss the theoretical interpretation of these MVAR-based connectivity measures, showing that each of them reflects a specific time-domain connectivity definition an…

Multivariate statisticsInformation transferTime FactorsArticle SubjectImmunology and Microbiology (all)Computer scienceBiostatisticslcsh:Computer applications to medicine. Medical informaticsGeneral Biochemistry Genetics and Molecular BiologyCausality (physics)HumansRepresentation (mathematics)Parametric statisticsBiochemistry Genetics and Molecular Biology (all)General Immunology and MicrobiologyMedicine (all)Applied MathematicsMedicine (all); Modeling and Simulation; Immunology and Microbiology (all); Biochemistry Genetics and Molecular Biology (all); Applied MathematicsElectroencephalographySignal Processing Computer-AssistedGeneral MedicineCoherence (statistics)Nonlinear DynamicsAutoregressive modelModeling and SimulationFrequency domainSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaMultivariate AnalysisLinear Modelslcsh:R858-859.7AlgorithmResearch ArticleComputational and Mathematical Methods in Medicine
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Decomposing the transfer entropy to quantify lag-specific Granger causality in cardiovascular variability.

2013

We present a modification of the well known transfer entropy (TE) which makes it able to detect, besides the direction and strength of the information transfer between coupled processes, its exact timing. The approach follows a decomposition strategy which identifies--according to a lag-specific formulation of the concept of Granger causality--the set of time delays carrying significant information, and then assigns to each of these delays an amount of information transfer such that the total contribution yields the overall TE. We propose also a procedure for the practical estimation from time series data of the relevant delays and lag-specific TE in both bivariate and multivariate settings…

Multivariate statisticsMathematical optimizationInformation transferMedicine (all)LagEntropyBivariate analysisCardiovascular Physiological PhenomenaGranger causalitySettore ING-INF/06 - Bioingegneria Elettronica E InformaticaMultivariate AnalysisEntropy (information theory)HumansTransfer entropyComputer SimulationTime seriesAlgorithmsMathematicsAnnual 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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Algorithms for the inference of causality in dynamic processes: Application to cardiovascular and cerebrovascular variability

2015

This study faces the problem of causal inference in multivariate dynamic processes, with specific regard to the detection of instantaneous and time-lagged directed interactions. We point out the limitations of the traditional Granger causality analysis, showing that it leads to false detection of causality when instantaneous and time-lagged effects coexist in the process structure. Then, we propose an improved algorithm for causal inference that combines the Granger framework with the approach proposed by Pearl for the study of causality among multiple random variables. This new approach is compared with the traditional one in theoretical and simulated examples of interacting processes, sho…

Multivariate statisticsProcess (engineering)Computer scienceBiomedical EngineeringInferenceHealth InformaticsMachine learningcomputer.software_genreHeart RateEconometricsHumansArterial PressureComputer Simulation1707Granger causality analysisSeries (mathematics)business.industryBrainHeartCausalityCausalityCerebrovascular CirculationCausal inferenceSignal ProcessingSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaArtificial intelligencebusinesscomputerRandom variableAlgorithms2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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Non-uniform multivariate embedding to assess the information transfer in cardiovascular and cardiorespiratory variability series

2012

The complexity of the short-term cardiovascular control prompts for the introduction of multivariate (MV) nonlinear time series analysis methods to assess directional interactions reflecting the underlying regulatory mechanisms. This study introduces a new approach for the detection of nonlinear Granger causality in MV time series, based on embedding the series by a sequential, non-uniform procedure, and on estimating the information flow from one series to another by means of the corrected conditional entropy. The approach is validated on short realizations of linear stochastic and nonlinear deterministic processes, and then evaluated on heart period, systolic arterial pressure and respira…

Multivariate statisticsSupine positionMultivariate analysisQuantitative Biology::Tissues and OrgansTime delay embeddingPhysics::Medical PhysicsPostureBlood PressureHealth InformaticsCardiovascular Physiological PhenomenaGranger causalityPosition (vector)StatisticsHumansCardiovascular interactionMathematicsConditional entropySeries (mathematics)RespirationModels CardiovascularReproducibility of ResultsSignal Processing Computer-AssistedComputer Science Applications1707 Computer Vision and Pattern RecognitionComputer Science ApplicationsNonlinear systemNonlinear DynamicsMultivariate AnalysisSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityMultivariate time serieConditional entropyAlgorithmAlgorithms
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Measuring frequency domain granger causality for multiple blocks of interacting time series

2011

In the past years, several frequency-domain causality measures based on vector autoregressive time series modeling have been suggested to assess directional connectivity in neural systems. The most followed approaches are based on representing the considered set of multiple time series as a realization of two or three vector-valued processes, yielding the so-called Geweke linear feedback measures, or as a realization of multiple scalar-valued processes, yielding popular measures like the directed coherence (DC) and the partial DC (PDC). In the present study, these two approaches are unified and generalized by proposing novel frequency-domain causality measures which extend the existing meas…

Multivariate statisticsTime FactorsGeneral Computer ScienceLogarithmScalar (mathematics)Complex systemTopologyModels BiologicalNeurophysiological time serieBlock-based connectivity analysiGranger causalityStatisticsHumansComputer SimulationDirected coherenceMathematicsNumerical analysisPartial directed coherenceBrainElectroencephalographyVector autoregressive (VAR) modelBrain WavesCausalityAutoregressive modelFrequency domainComputer ScienceSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityAlgorithmsBiotechnologyBiological Cybernetics
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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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Multivariate Frequency Domain Analysis of Causal Interactions in Physiological Time Series

2011

A common way of obtaining information about a physiological system is to measure one or more signals from the system, consider their temporal evolution in the form of numerical time series, and obtain quantitative indexes through the application of time series analysis techniques. While historical approaches to time series analysis were addressed to the study of single signals, recent advances have made it possible to study collectively the behavior of several signals measured simultaneously from the considered system. In fact, multivariate (MV) time series analysis is nowadays extensively used to characterize interdependencies among multiple signals collected from dynamical physiological s…

Multivariate statisticsmedicine.diagnostic_testComputer sciencebusiness.industryLinear modelPattern recognitionNeurophysiologyElectroencephalographyRespiratory flowCausality connectivity VAR modelsFrequency domainSettore ING-INF/06 - Bioingegneria Elettronica E InformaticamedicineArtificial intelligenceTime seriesbusinessTime complexity
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Testing different methodologies for Granger causality estimation: A simulation study

2021

Granger causality (GC) is a method for determining whether and how two time series exert causal influences one over the other. As it is easy to implement through vector autoregressive (VAR) models and can be generalized to the multivariate case, GC has spread in many different areas of research such as neuroscience and network physiology. In its basic formulation, the computation of GC involves two different regressions, taking respectively into account the whole past history of the investigated multivariate time series (full model) and the past of all time series except the putatively causal time series (restricted model). However, the restricted model cannot be represented through a finit…

Multivariate statisticsstate space modelsSeries (mathematics)Computer scienceGranger causality; state space modelsDynamical NetworksMultivariate Time SeriesReduction (complexity)Autoregressive modelGranger causalitySettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityState spaceConditioningTime seriesVector Autoregressive ProcessesAlgorithm2020 28th European Signal Processing Conference (EUSIPCO)
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"He vivido y me he vivido como valenciano"

2006

MundializaciónVidal-Beneyto JoséAnálisis intelectualChoque de civilizacionesHonoris CausaBIOGRAFÍAEncuentros culturalesOpiniónProblemática mundialIdentidad valencianaImprovisados demócratasVinculaciónPosición críticaVALENCIAValencianoDebates internacionalesGlobalizaciónMemoriaTierraReflexión geopolígicaJuntas DemocráticasRealidad contemporáneaFranquismoUniversitat de ValènciaParticipación políticaMediatizaciónJOSÉ VIDAL-BENEYTODigitalizaciónGobernación del mundoEntrevistasComunidad ValencianaAcciones: Acciones de una vida: Democracia (I)Debates culturales
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Lector in fabula. La narrazione come risorsa argomentativa nel Fedone.

2017

Muovendo da una analisi della celebre "pagina autobiografica" del Fedone, il saggio mette in evidenza la funzione strategica che il ricorso ad alcuni spedenti narrativi, acquista ai fini della costruzione del complesso ragionamento proposto da Socrate. La nascita del concetto di causa formale deve parecchio all'intreccio che la pagina platonica produce frana componnte narrativa e quella argomentativa.

Narrazione Argomentazione Causa formale Verità Seconda navigazione.Settore M-FIL/01 - Filosofia Teoretica
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