Search results for "spectral decomposition"

showing 4 items of 4 documents

Spectral decomposition of cerebrovascular and cardiovascular interactions in patients prone to postural syncope and healthy controls.

2022

We present a framework for the linear parametric analysis of pairwise interactions in bivariate time series in the time and frequency domains, which allows the evaluation of total, causal and instantaneous interactions and connects time- and frequency-domain measures. The framework is applied to physiological time series to investigate the cerebrovascular regulation from the variability of mean cerebral blood flow velocity (CBFV) and mean arterial pressure (MAP), and the cardiovascular regulation from the variability of heart period (HP) and systolic arterial pressure (SAP). We analyze time series acquired at rest and during the early and late phase of head-up tilt in subjects developing or…

Endocrine and Autonomic SystemsTime series analysisBlood PressureHeartBaroreflexCardiovascular SystemSyncopeCerebral autoregulationCellular and Molecular NeuroscienceHeart RateAutoregressive modelsCardiovascular controlCerebrovascular CirculationGranger causalitySettore ING-INF/06 - Bioingegneria Elettronica e InformaticaHumansNeurology (clinical)Spectral decompositionAutoregressive models; Cardiovascular control; Cerebral autoregulation; Granger causality; Spectral decomposition; Time series analysis;Autonomic neuroscience : basicclinical
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On the interpretability and computational reliability of frequency-domain Granger causality

2017

This Correspondence article is a comment which directly relates to the paper “A study of problems encountered in Granger causality analysis from a neuroscience perspective” (Stokes and Purdon, 2017). We agree that interpretation issues of Granger causality (GC) in neuroscience exist, partially due to the historically unfortunate use of the name “causality”, as described in previous literature. On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used) and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures based on a suboptimal use of them. Furthermore, s…

FOS: Computer and information sciences0301 basic medicineTheoretical computer scienceImmunology and Microbiology (all)Computer scienceTime series analysiMathematics - Statistics TheoryStatistics Theory (math.ST)Statistics - ApplicationsGeneral Biochemistry Genetics and Molecular BiologyMethodology (stat.ME)Causality (physics)03 medical and health sciences0302 clinical medicinegranger causalityGranger causalityCorrespondenceFOS: MathematicsApplications (stat.AP)Physiological oscillationGeneral Pharmacology Toxicology and PharmaceuticsTime seriessignal processingStatistical Methodologies & Health Informaticsfrequency-domain connectivityReliability (statistics)Statistics - MethodologyInterpretabilityGranger-Geweke causalityBiochemistry Genetics and Molecular Biology (all)Interpretation (logic)General Immunology and Microbiologybrain connectivityGeneral MedicineArticlesvector autoregressive models030104 developmental biologyMathematics and StatisticsWildcardVector autoregressive modelPharmacology Toxicology and Pharmaceutics (all)Frequency domaintime series analysisspectral decompositionSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaBrain connectivity; Directed coherence; Frequency-domain connectivity; Granger-Geweke causality; Physiological oscillations; Spectral decomposition; Time series analysis; Vector autoregressive models; Biochemistry Genetics and Molecular Biology (all); Immunology and Microbiology (all); Pharmacology Toxicology and Pharmaceutics (all)directed coherence030217 neurology & neurosurgeryphysiological oscillations
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Comparison of frequency domain measures based on spectral decomposition for spontaneous baroreflex sensitivity assessment after Acute Myocardial Infa…

2021

Abstract The objective of this study is to present a new method to assess in the frequency domain the directed interactions between the spontaneous variability of systolic arterial pressure (SAP) and heart period (HP) from their linear model representation, and to apply it for studying the baroreflex control of arterial pressure in healthy physiological states and after acute myocardial infarction (AMI). The method is based on pole decomposition of the model transfer function and on the following evaluation of causal measures of coupling and gain from the poles associated to low frequency (0.04−0.15 Hz) oscillatory components. It is compared with traditional non-causal approaches for the sp…

medicine.medical_specialty0206 medical engineeringBiomedical EngineeringHealth Informatics02 engineering and technologyAcute myocardial infarctionBaroreflexSettore ING-INF/01 - ElettronicaMatrix decomposition03 medical and health sciences0302 clinical medicineInternal medicinemedicineSpectral analysiscardiovascular diseasesMyocardial infarctionSensitivity (control systems)Spectral decompositionbusiness.industryHead-up tiltLinear modelBaroreflexmedicine.disease020601 biomedical engineeringFrequency domainCausalityBlood pressureFrequency domainCardiovascular controlSignal ProcessingSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaCardiologybusiness030217 neurology & neurosurgery
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Wiener-Granger Causality in Network Physiology with Applications to Cardiovascular Control and Neuroscience

2016

Since the operative definition given by C. W. J. Granger of an idea expressed by N. Wiener, the Wiener–Granger causality (WGC) has been one of the most relevant concepts exploited by modern time series analysis. Indeed, in networks formed by multiple components, working according to the notion of segregation and interacting with each other according to the principle of integration, inferring causality has opened a window on the effective connectivity of the network and has linked experimental evidences to functions and mechanisms. This tutorial reviews predictability improvement, information-based and frequency domain methods for inferring WGC among physiological processes from multivariate…

nonlinear dynamicComputer scienceReliability (computer networking)Biomedical signal processingPhysiologyCardiovascular controldynamical systemdirectionalityGranger causalitymultivariate regression modelingtime series analysiPredictabilityTime seriesElectrical and Electronic EngineeringStatistical hypothesis testingbusiness.industryheart rate variabilitytransfer entropypartial directed coherencepredictioncoupling strengthCausalityconditional mutual informationFrequency domainspectral decompositionSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaArtificial intelligencebusinesscomplexityNeuroscience
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