Search results for "multivariate time series analysis"

showing 8 items of 8 documents

Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators

2021

One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…

Artificial neural networks; Chaotic oscillators; Granger causality; Multivariate time series analysis; Network physiology; Penalized regression techniques; Remote synchronization; State-space models; Stochastic gradient descent L1; Vector autoregressive modelGeneral Computer ScienceDynamical systems theoryComputer science02 engineering and technologyChaotic oscillatorsPenalized regression techniquesNetwork topologySettore ING-INF/01 - ElettronicaMultivariate time series analysisVector autoregression03 medical and health sciences0302 clinical medicineScientific Computing and Simulation0202 electrical engineering electronic engineering information engineeringRepresentation (mathematics)Optimization Theory and ComputationNetwork physiologyState-space modelsArtificial neural networkArtificial neural networksData ScienceTheory and Formal MethodsQA75.5-76.95Stochastic gradient descent L1Granger causality State-space models Vector autoregressive model Artificial neural networks Stochastic gradient descent L1 Multivariate time series analysis Network physiology Remote synchronization Chaotic oscillators Penalized regression techniquesRemote synchronizationStochastic gradient descentAutoregressive modelAlgorithms and Analysis of AlgorithmsVector autoregressive modelElectronic computers. Computer scienceSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causality020201 artificial intelligence & image processingGradient descentAlgorithm030217 neurology & neurosurgeryPeerJ Computer Science
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Information Decomposition in Multivariate Systems: Definitions, Implementation and Application to Cardiovascular Networks

2016

The continuously growing framework of information dynamics encompasses a set of tools, rooted in information theory and statistical physics, which allow to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of complex networks. Building on the most recent developments in this field, this work designs a complete approach to dissect the information carried by the target of a network of multiple interacting systems into the new information produced by the system, the information stored in the system, and the information transferred to it from the other systems; information storage and transfer are then further decomposed into amou…

Information transferDynamical systems theoryComputer scienceGeneral Physics and Astronomylcsh:AstrophysicsInformation theorycomputer.software_genreMachine learning01 natural sciencesEntropy - Cardiorespiratory interactions - Dynamical systems -cardiovascular interactions03 medical and health sciencessymbols.namesake0302 clinical medicinelcsh:QB460-4660103 physical sciencesinformation transferEntropy (information theory)lcsh:Science010306 general physicsGaussian processautoregressive processesmultivariate time series analysisbusiness.industryautonomic nervous systemredundancy and synergycardiorespiratory interactionsdynamical systemsComplex networkNetwork dynamicslcsh:QC1-999autonomic nervous system; autoregressive processes; cardiorespiratory interactions; cardiovascular interactions; Granger causality; dynamical systems; information dynamics; information transfer; redundancy and synergy; multivariate time series analysisAutoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalitysymbolslcsh:QArtificial intelligenceData mininginformation dynamicsbusinesscomputerlcsh:Physics030217 neurology & neurosurgeryEntropy; Volume 19; Issue 1; Pages: 5
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Information decomposition in the frequency domain: a new framework to study cardiovascular and cardiorespiratory oscillations

2021

While cross-spectral and information-theoretic approaches are widely used for the multivariate analysis of physiological time series, their combined utilization is far less developed in the literature. This study introduces a framework for the spectral decomposition of multivariate information measures, which provides frequency-specific quantifications of the information shared between a target and two source time series and of its expansion into amounts related to how the sources contribute to the target dynamics with unique, redundant and synergistic information. The framework is illustrated in simulations of linearly interacting stochastic processes, showing how it allows us to retrieve …

Multivariate statisticsMultivariate analysisComputer scienceGeneral MathematicsGeneral Physics and AstronomyBlood PressureCardiovascular SystemMatrix decompositionHeart RateDecomposition (computer science)HumansHeart rate variabilityStatistical physicsSeries (mathematics)Stochastic processRespirationautonomic nervous systemGeneral EngineeringMultivariate time series analysisheart rate variabilityredundancy and synergyCardiorespiratory fitnesscoherence function multivariate time-series analysiTerm (time)Autonomic nervous systemInformation dynamicFrequency domainMultivariate AnalysisBiological system
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Basic cardiovascular variability signals: mutual directed interactions explored in the information domain.

2017

The study of short-term cardiovascular interactions is classically performed through the bivariate analysis of the interactions between the beat-to-beat variability of heart period (RR interval from the ECG) and systolic blood pressure (SBP). Recent progress in the development of multivariate time series analysis methods is making it possible to explore how directed interactions between two signals change in the context of networks including other coupled signals. Exploiting these advances, the present study aims at assessing directional cardiovascular interactions among the basic variability signals of RR, SBP and diastolic blood pressure (DBP), using an approach which allows direct compar…

MaleMultivariate statisticsAdolescentPhysiologySystole0206 medical engineeringBiomedical EngineeringBiophysicsContext (language use)Blood Pressure02 engineering and technologyBivariate analysisBaroreflex03 medical and health sciencesElectrocardiography0302 clinical medicineinformation domainDiastoleHeart RatePhysiology (medical)StatisticsHumansbaroreflexMathematicsResting state fMRIheart rate variabilityMultivariate time series analysiscomplex system020601 biomedical engineeringcardiovascular oscillationBlood pressureBiophysicInformation domainSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaFemaleblood pressure variability030217 neurology & neurosurgeryHumancirculatory and respiratory physiologyPhysiological measurement
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Information transfer and information modification to identify the structure of cardiovascular and cardiorespiratory networks

2017

To fully elucidate the complex physiological mechanisms underlying the short-term autonomic regulation of heart period (H), systolic and diastolic arterial pressure (S, D) and respiratory (R) variability, the joint dynamics of these variables need to be explored using multivariate time series analysis. This study proposes the utilization of information-theoretic measures to measure causal interactions between nodes of the cardiovascular/cardiorespiratory network and to assess the nature (synergistic or redundant) of these directed interactions. Indexes of information transfer and information modification are extracted from the H, S, D and R series measured from healthy subjects in a resting…

medicine.medical_specialtyInformation transferPosture0206 medical engineeringBiomedical EngineeringBlood PressureHealth Informatics02 engineering and technologycomputer.software_genreCardiovascular SystemDiastolic arterial pressureAutonomic regulation03 medical and health sciences0302 clinical medicineHeart RateInternal medicineBayesian multivariate linear regressionmedicine1707Resting state fMRIbusiness.industryRespirationMultivariate time series analysisHealthy subjectsCardiorespiratory fitness020601 biomedical engineeringSignal ProcessingSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaLinear ModelsCardiologyData miningbusinesscomputer030217 neurology & neurosurgery2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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Information Transfer in Linear Multivariate Processes Assessed through Penalized Regression Techniques: Validation and Application to Physiological N…

2020

The framework of information dynamics allows the dissection of the information processed in a network of multiple interacting dynamical systems into meaningful elements of computation that quantify the information generated in a target system, stored in it, transferred to it from one or more source systems, and modified in a synergistic or redundant way. The concepts of information transfer and modification have been recently formulated in the context of linear parametric modeling of vector stochastic processes, linking them to the notion of Granger causality and providing efficient tools for their computation based on the state&ndash

conditional transfer entropyInformation transferlinear predictionDynamical systems theoryComputer scienceState–space modelsGeneral Physics and Astronomylcsh:AstrophysicsNetwork topologycomputer.software_genrenetwork physiology01 natural sciencesArticle03 medical and health sciences0302 clinical medicinepenalized regression techniquelcsh:QB460-4660103 physical sciencesEntropy (information theory)Statistics::Methodologylcsh:Science010306 general physicspartial information decompositionmultivariate time series analysisinformation dynamics; partial information decomposition; entropy; conditional transfer entropy; network physiology; multivariate time series analysis; State–space models; vector autoregressive model; penalized regression techniques; linear predictionState–space modellcsh:QC1-999multivariate time series analysiInformation dynamicData pointpenalized regression techniquesAutoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaParametric modelOrdinary least squaresvector autoregressive modellcsh:QData mininginformation dynamicsentropycomputerlcsh:Physics030217 neurology & neurosurgery
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Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes

2017

Exploiting the theory of state space models, we derive the exact expressions of the information transfer, as well as redundant and synergistic transfer, for coupled Gaussian processes observed at multiple temporal scales. All of the terms, constituting the frameworks known as interaction information decomposition and partial information decomposition, can thus be analytically obtained for different time scales from the parameters of the VAR model that fits the processes. We report the application of the proposed methodology firstly to benchmark Gaussian systems, showing that this class of systems may generate patterns of information decomposition characterized by prevalently redundant or sy…

FOS: Computer and information sciencesInformation transferComputer scienceGaussianSocial SciencesGeneral Physics and AstronomyInformation theory01 natural sciences010305 fluids & plasmasState spaceStatistical physicslcsh:Scienceinformation theorymultiscale entropylcsh:QC1-999Interaction informationMathematics and Statisticssymbolsinformation dynamicsInformation dynamics; Information transfer; Multiscale entropy; Multivariate time series analysis; Redundancy and synergy; State space models; Vector autoregressive models; Physics and Astronomy (all)information dynamics; information transfer; multiscale entropy; multivariate time series analysis; redundancy and synergy; state space models; vector autoregressive modelsMultivariate time series analysiMathematics - Statistics Theorylcsh:AstrophysicsStatistics Theory (math.ST)Statistics - ApplicationsMethodology (stat.ME)symbols.namesakePhysics and Astronomy (all)0103 physical scienceslcsh:QB460-466FOS: Mathematicsinformation transferRelevance (information retrieval)Applications (stat.AP)Transfer Entropy010306 general physicsGaussian processStatistics - MethodologyState space modelstate space modelsmultivariate time series analysisredundancy and synergyvector autoregressive modelsInformation dynamicVector autoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaTransfer entropylcsh:Qlcsh:PhysicsEntropy
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Assessing Complexity in Physiological Systems through Biomedical Signals Analysis

2020

The idea that most physiological systems are complex has become increasingly popular in recent decades [...]

information flowComputer sciencebrainMultivariate time series analysisGeneral Physics and Astronomylcsh:AstrophysicsData sciencelcsh:QC1-999Fetal heart rateFuzzy entropyEditorialmultifractalitymultiscaleSettore ING-INF/06 - Bioingegneria Elettronica E Informaticalcsh:QB460-466Autonomic nervous functionBrain; Cardiovascular system; Entropy; Information flow; Multifractality; Multiscalecardiovascular systemHypobaric hypoxialcsh:QInformation dynamicsentropylcsh:Sciencelcsh:PhysicsEntropy
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