0000000000220747
AUTHOR
L. Faes
Transient behavior of cardiorespiratory interactions towards the onset of epileptic seizures
Epileptic 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…
Comparing model-free and model-based transfer entropy estimators in cardiovascular variability
Information flow between heart period (T), systolic pressure (S) and respiration (R) variability in a head-up tilt (HUT) protocol is assessed by transfer entropy (TE). Two estimates of TE are compared: the model-based (MB) approach using linear regression under the Gaussian assumption, and the model-free (MF) approach combining binning estimates of entropy and non-uniform delay embedding. The approaches were applied to 300-beats series of T, S, R measured in the supine (su) and upright (up) positions during HUT. Both MB and MF approaches detected a unidirectional information transfer from R to T and from R to S, and a significant decrease of the TE from R to T, as well as a significant incr…
Separating respiratory influences from the tachogram: Methods and their sensitivity to the type of respiratory signal
Respiration is one of the main modulators causing heart rate variability (HRV). However, when interpreting studies of HRV, the effect of respiration is largely ignored. We, therefore, previously proposed to take respiratory influences into account by separating the tachogram in a component that is related to respiration and one that contains all residual variations. In this study, we aim to investigate the sensitivity of two of such separation methods, i.e. one based on an ARMAX model and another one based on orthogonal subspace projection (OSP), towards different respiratory signal types, such as nasal airflow (the reference), thoracic and abdominal efforts, and three ECG-derived respirato…
Evaluation of a nonlinear prediction algorithm quantifying regularity, synchronization and directionality in short cardiovascular variability series
An unifying approach evaluating complex dynamics and dynamical interactions in short bivariate time series is presented. The method performs nearest neighbor local linear prediction to estimate regularity, synchronization and directionality of two interacting time series. It was implemented through a specific cross-validation procedure which allowed an unconstrained embedding of the series and a full exploitation of the available data to maximize the accuracy of prediction. The approach was evaluated by simulations of stochastic (autoregressive processes) and deterministic (Henon maps) models in which uncoupled, unidirectionally coupled and bidirectionally coupled dynamics were generated. T…
Exploring causal interactions between blood pressure and RR interval at the respiratory frequency
The mechanisms underlying the relationship between RR interval and systolic arterial pressure (SAP) variability at the respiratory frequency are still object of discussion. In this study, the information on directionality provided by causal cross-spectral analysis was exploited to infer possible influences of respiration on cardiovascular parameters variability. The ability of causal analysis to account for directionality in RR-SAP interrelationships in presence of respiratory exogenous effects was first tested on model simulations. Hence, real data measured on healthy subjects during spontaneous and paced breathing at 0.25 Hz were analysed. The results obtained in real data were consistent…
Gradients of O-information: Low-order descriptors of high-order dependencies
O-information is an information-theoretic metric that captures the overall balance between redundant and synergistic information shared by groups of three or more variables. To complement the global assessment provided by this metric, here we propose the gradients of the O-information as low-order descriptors that can characterise how high-order effects are localised across a system of interest. We illustrate the capabilities of the proposed framework by revealing the role of specific spins in Ising models with frustration, and on practical data analysis on US macroeconomic data. Our theoretical and empirical analyses demonstrate the potential of these gradients to highlight the contributio…