620 research outputs found
Quantifying the complexity of short-term heart period variability through K nearest neighbor local linear prediction
No full textThe complexity of short-term heart period (HP) variability was quantified exploiting the paradigm that associates the degree of unpredictability of a time series to its dynamical complexity. Complexity was assessed through k-nearest neighbor local linear prediction. A proper selection of the parameter k allowed us to perform either linear or nonlinear prediction, and the comparison of the two approaches to infer the presence of nonlinear dynamics. The method was validated on simulations reproducing linear and nonlinear time series with varying levels of predictability. It was then applied to HP variability series measured from healthy subjects during head-up tilt test, showing that short-term HP complexity increases significantly from the supine to the upright position, and that nonlinearities are involved in the generation of HP dynamics in both positions
Doxorubicin Impairs Smooth Muscle Cell Contraction: Novel Insights in Vascular Toxicity
No full textCitation: Bosman, M.; Krüger, D.N.; Favere, K.; Wesley, C.D.; Neutel, C.H.G.; Van Asbroeck, B.; Diebels, O.R.; Faes, B.; Schenk, T.J.; Martinet, W.; et al. Doxorubicin Impairs Smooth Muscle Cell Contraction: Novel Insights in Vascular Toxicity
Assessing Connectivity in the Presence of Instantaneous Causality
No full textThis chapter is devoted to a discussion of the impact of instantaneous causality on the computation of frequency domain connectivity measures. Instantaneous causality (IC) refers to interactions between two observed time series which occur within the same time lag
Linear and nonlinear parametric model identification to assess granger causality in short-term cardiovascular interactions
No full textWe assessed directional relationships between short RR interval and systolic arterial pressure (SAP) variability series according to the concept of Granger causality. Causality was quantified as the predictability improvement (PI) of a time series obtained when samples of the other series were used for prediction, i.e. moving from autoregressive (AR) to AR exogenous (ARX) prediction. AR and ARX predictions were performed both by linear and nonlinear parametric models. The PIs of RR given SAP and of SAP given RR, measuring baroreflex and mechanical couplings, were calculated in 15 healthy subjects in the resting supine and upright tilt positions. Using nonlinear models we found a bilateral interaction between the two series, unbalanced towards the mechanical direction at rest and balanced after tilt. The utilization of linear AR and ARX models led to higher prediction accuracy but comparable trends of predictability and causality measures
K-Nearest neighbour local linear prediction of scalp EEG activity during intermittent photic stimulation
No full textThe characterization of the EEG response to photic stimulation (PS) is an important issue with significant clinical relevance. This study aims to quantify and map the complexity of the EEG during PS, where complexity is measured as the degree of unpredictability resulting from local linear prediction. EEG activity was recorded with eyes closed (EC) and eyes open (EO) during resting and PS at 5, 10, and 15. Hz in a group of 30 healthy subjects and in a case-report of a patient suffering from cerebral ischemia. The mean squared prediction error (MSPE) resulting from k-nearest neighbour local linear prediction was calculated in each condition as an index of EEG unpredictability. The linear or nonlinear nature of the system underlying EEG activity was evaluated quantifying MSPE as a function of the neighbourhood size during local linear prediction, and by surrogate data analysis as well. Unpredictability maps were obtained for each subject interpolating MSPE values over a schematic head representation. Results on healthy subjects evidenced: (i) the prevalence of linear mechanisms in the generation of EEG dynamics, (ii) the lower predictability of EO EEG, (iii) the desynchronization of oscillatory mechanisms during PS leading to increased EEG complexity, (iv) the entrainment of alpha rhythm during EC obtained by 10. Hz PS, and (v) differences of EEG predictability among different scalp regions. Ischemic patient showed different MSPE values in healthy and damaged regions. The EEG predictability decreased moving from the early acute stage to a stage of partial recovery. These results suggest that nonlinear prediction can be a useful tool to characterize EEG dynamics during PS protocols, and may consequently constitute a complement of quantitative EEG analysis in clinical applications. © 2010 IPEM
On the choice of the mesh for the analysis of geostatistical data using R-INLA
No full textMany methods used in spatial statistics are computationally demanding, and so, the development of more computationally efficient methods has received attention. A important development is the integrated nested Laplace approximation method which is carry out Bayesian analysis more efficiently This method, for geostatistical data, is done considering the SPDE approach that requires the creation of a mesh overlying the study area and all the obtained results depend on it. The impact of the mesh on inference and prediction is investigated through simulations. As there is no formal procedure to specify it, we investigate a guideline to create an optimal mesh.The first author acknowledge the financial support of the "Ciencia sem Fronteiras" program of CNPq (Brazil) under the process number 200573/2015-2. Support from the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy) is also gratefully acknowledged by the second and third author.Ribeiro, PJ (reprint author), Univ Sao Paulo, Dept Ciencias Exatas, BR-13418900 Piracicaba, SP, Brazil.
[email protected]
Mutual nonlinear prediction as a tool to evaluate coupling strength and directionality in bivariate time series: Comparison among different strategies based on k nearest neighbors
No full textWe compare the different existing strategies of mutual nonlinear prediction regarding their ability to assess the coupling strength and directionality of the interactions in bivariate time series. Under the common framework of k -nearest neighbor local linear prediction, we test three approaches based on cross prediction, mixed prediction, and predictability improvement. The measures of interdependence provided by these approaches are first evaluated on short realizations of bivariate time series generated by coupled Henon models, investigating also the effects of noise. The usefulness of the three mutual nonlinear prediction schemes is then assessed in a common physiological application during known conditions of interaction-i.e., the analysis of the interdependence between heart rate and arterial pressure variability in healthy humans during supine resting and passive head-up tilting. Based on both simulation results and physiological interpretability of cardiovascular results, we conclude that cross prediction is valuable to quantify the coupling strength and predictability improvement to elicit directionality of the interactions in short and noisy bivariate time serie
Transient behavior of cardiorespiratory interactions towards the onset of epileptic seizures
No full textEpileptic seizures are typically related to autonomic dysfunction. During seizures, the cardiac and respiratory mechanisms are deeply affected. This effect of epilepsy can also occur a few seconds before the seizure onset in the EEG. In addition, the interaction between respiration and heart rate is also expected to be affected. This study aims to determine whether the cardiorespiratory interactions change during seizures, and more importantly if they show a transient behavior towards the seizure onset. This is done by means of a time series method based on entropy decomposition applied to ECG and respiratory data. Here, the information carried by the heart rate that can be predicted by its own past, or by the past of the respiration, or by a combination of the two, is quantified. It is shown that cardiorespiratory interactions also change even before the onset of focal, and absence seizures. This suggests that early detection of focal seizures can be improved, and that detection of seizures without a clear effect on the heart rate (i.e. absence) can also be detected. In tonic/tonic-clonic seizures no consistent significant change in the autonomic controls was found
Integrated Nested Laplace Approximation as a new approximation method for the combined model: A simulation study
No full text© 2017, © 2017 Taylor & Francis Group, LLC. The combined model accounts for different forms of extra-variability and has traditionally been applied in the likelihood framework, or in the Bayesian setting via Markov chain Monte Carlo. In this article, integrated nested Laplace approximation is investigated as an alternative estimation method for the combined model for count data, and compared with the former estimation techniques. Longitudinal, spatial, and multi-hierarchical data scenarios are investigated in three case studies as well as a simulation study. As a conclusion, integrated nested Laplace approximation provides fast and precise estimation, while avoiding convergence problems often seen when using Markov chain Monte Carlo.sponsorship: Financial support from the IAP research network #P7/06 of the Belgian Government (Belgian Science Policy) and the Research Foundation Flanders (12S7217N) is gratefully acknowledged. (IAP research network of the Belgian Government (Belgian Science Policy)|P7/06, Research Foundation Flanders|12S7217N)status: Published onlin
A method for the time-varying nonlinear prediction of complex nonstationary biomedical signals
No full textA method to perform time-varying (TV) nonlinear prediction of biomedical signals in the presence of nonstationarity is presented in this paper. The method is based on identification of TV autoregressive models through expansion of the TV coefficients onto a set of basis functions and on k-nearest neighbor local linear approximation to perform nonlinear prediction. The approach provides reasonable nonlinear prediction even for TV deterministic chaotic signals, which has been a daunting task to date. Moreover, the method is used in conjunction with a TV surrogate method to provide statistical validation that the presence of nonlinearity is not due to nonstationarity itself. The approach is tested on simulated linear and nonlinear signals reproducing both time-invariant (TIV) and TV dynamics to assess its ability to quantify TIV and TV degrees of predictability and detect nonlinearity. Applicative examples relevant to heart rate variability and EEG analyses are then illustrated
- …
