Search results for "Heart period variability"

showing 4 items of 4 documents

Conditional Self-Entropy and Conditional Joint Transfer Entropy in Heart Period Variability during Graded Postural Challenge.

2015

Self-entropy (SE) and transfer entropy (TE) are widely utilized in biomedical signal processing to assess the information stored into a system and transferred from a source to a destination respectively. The study proposes a more specific definition of the SE, namely the conditional SE (CSE), and a more flexible definition of the TE based on joint TE (JTE), namely the conditional JTE (CJTE), for the analysis of information dynamics in multivariate time series. In a protocol evoking a gradual sympathetic activation and vagal withdrawal proportional to the magnitude of the orthostatic stimulus, such as the graded head-up tilt, we extracted the beat-to-beat spontaneous variability of heart per…

AdultMalemedicine.medical_specialtygenetic structuresEntropyPosturelcsh:MedicineMedicine (all); Biochemistry Genetics and Molecular Biology (all); Agricultural and Biological Sciences (all)Orthostatic vital signsYoung AdultInternal medicineLinear regressionmedicineHumanslcsh:ScienceMathematicsBiochemistry Genetics and Molecular Biology (all)MultidisciplinaryMedicine (all)lcsh:RHealthy subjectsHeart period variabilityHeartSignal Processing Computer-AssistedMiddle AgedBlood pressureAgricultural and Biological Sciences (all)Settore ING-INF/06 - Bioingegneria Elettronica E InformaticaReflexCardiologylcsh:QTransfer entropyFemaleInformation dynamicsResearch ArticlePloS one
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Assessing complexity and causality in heart period variability through a model-free data-driven multivariate approach

2017

The aim of this study is to emphasize the importance of model-free data-driven mul- tivariate approaches in describing HP variability and cardiovascular control mechanisms responsible for inducing HP changes via modifications of different cardiovascular vari- ables such as SAP and RESP. The goal was achieved through the application, a previously proposed model-free data-driven multivariate framework devised to assess complexity and causality over a multivariate set composed by several, simultaneously recorded, car- diovascular variability series (Porta et al., 2014). The approach was applied to assess the complexity of the cardiac control, through the evaluation of the amount of irregularit…

Causality (physics)Complexity causalityMultivariate statisticsSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaEconometricsHeart period variabilityModel freeMathematicsData-driven
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Entropy-Based Detection of Complexity and Nonlinearity in Short-Term Heart Period Variability under different Physiopathological States

2020

We compare different estimators of a popular en-tropy-based nonlinear dynamic measure, i.e. the conditional entropy (CE), as regards their ability to assess the complexity and nonlinearity of short-term heart rate variability (HRV). The CE is computed using binning, kernel and nearest neighbor entropy estimators in HRV time series measured from young, old and post-myocardial infarction patients studied at rest and during orthostatic stress. We find that the three estimators yield similar patterns of CE, but different patterns of nonlinear dynamics, across groups and conditions. These results suggest that the strategy for CE estimation is not crucial for the quantification of complexity, but…

Conditional entropynearest neighborHeart period variabilityEstimatork-nearest neighbors algorithmConditional entropy (CE)Nonlinear systemStatisticsSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaEntropy (information theory)Heart rate variabilitynonlinear analysis methodTime seriescomplexityheart rate variability (HRV)Mathematics
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Information-Theoretic Analysis of Cardiorespiratory Interactions during Apneic Events in Sleep

2020

In this work, measures of information dynamics are used to describe the dynamics of heart rate and cardiorespiratory interaction associated to sleep breathing disorders. In a large group of patients reporting repeated episodes of hypopneas, apneas (central, obstructive, mixed) and respiratory effort related arousals (RERA), we computed information storage of heart period variability and information transfer from heart period to airflow amplitude before, during and after each event. We find a general tendency to decrease of the information storage, suggesting higher complexity of the cardiac dynamics. The information transfer decreased during apneic events, and increased during milder disord…

medicine.medical_specialtymedicine.diagnostic_testbusiness.industrySleep apneasSleep apneaHeart period variabilityCardiorespiratory fitnessmedicine.diseaseInternal medicineHeart rateSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaCardiologyRespiratory effortMedicineInformation dynamicsVagal tonebusinessElectrocardiography
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