Search results for "conditional entropy"
showing 10 items of 24 documents
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…
Investigating the mechanisms of cardiovascular and cerebrovascular regulation in orthostatic syncope through an information decomposition strategy
2012
Some previous evidence suggests that postural related syncope is associated with defective mechanisms of cerebrovascular (CB) and cardiovascular (CV) control. We characterized the information processing in short-term CB regulation, from the variability of mean cerebral blood flow velocity (CBFV) and mean arterial pressure (AP), and in CV regulation, from the variability of heart period (HP) and systolic AP (SAP), in ten young subjects developing orthostatic syncope in response to prolonged head-up tilt testing. We exploited a novel information-theoretic approach that decomposes the information associated with a variability series into three amounts: the information stored in the series, the…
Univariate and multivariate conditional entropy measures for the characterization of short-term cardiovascular complexity under physiological stress
2017
Objective: A defining feature of physiological systems under the neuroautonomic regulation is their dynamical complexity. The most common approach to assess physiological complexity from short-term recordings, i.e. to compute the rate of entropy generation of an individual system by means of measures of conditional entropy (CE), does not consider that complexity may change when the investigated system is part of a network of physiological interactions. This study aims at extending the concept of short-term complexity towards the perspective of network physiology, defining multivariate CE measures whereby multiple physiological processes are accounted for in the computation of entropy rates.…
Comparison of short-term heart rate variability indexes evaluated through electrocardiographic and continuous blood pressure monitoring
2019
Heart rate variability (HRV) analysis represents an important tool for the characterization of complex cardiovascular control. HRV indexes are usually calculated from electrocardiographic (ECG) recordings after measuring the time duration between consecutive R peaks, and this is considered the gold standard. An alternative method consists of assessing the pulse rate variability (PRV) from signals acquired through photoplethysmography, a technique also employed for the continuous noninvasive monitoring of blood pressure. In this work, we carry out a thorough analysis and comparison of short-term variability indexes computed from HRV time series obtained from the ECG and from PRV time series …
Optimal Placement of Pressure Sensors Using Fuzzy DEMATEL-Based Sensor Influence
2020
[EN] Nowadays, optimal sensor placement (OSP) for leakage detection in water distribution networks is a lively field of research, and a challenge for water utilities in terms of network control, management, and maintenance. How many sensors to install and where to install them are crucial decisions to make for those utilities to reach a trade-off between efficiency and economy. In this paper, we address the where-to-install-them part of the OSP through the following elements: nodes' sensitivity to leakage, uncertainty of information, and redundancy through conditional entropy maximisation. We evaluate relationships among candidate sensors in a network to get a picture of the mutual influenc…
MuTE: a MATLAB toolbox to compare established and novel estimators of the multivariate transfer entropy.
2014
A challenge for physiologists and neuroscientists is to map information transfer between components of the systems that they study at different scales, in order to derive important knowledge on structure and function from the analysis of the recorded dynamics. The components of physiological networks often interact in a nonlinear way and through mechanisms which are in general not completely known. It is then safer that the method of choice for analyzing these interactions does not rely on any model or assumption on the nature of the data and their interactions. Transfer entropy has emerged as a powerful tool to quantify directed dynamical interactions. In this paper we compare different ap…
Non-uniform multivariate embedding to assess the information transfer in cardiovascular and cardiorespiratory variability series
2012
The complexity of the short-term cardiovascular control prompts for the introduction of multivariate (MV) nonlinear time series analysis methods to assess directional interactions reflecting the underlying regulatory mechanisms. This study introduces a new approach for the detection of nonlinear Granger causality in MV time series, based on embedding the series by a sequential, non-uniform procedure, and on estimating the information flow from one series to another by means of the corrected conditional entropy. The approach is validated on short realizations of linear stochastic and nonlinear deterministic processes, and then evaluated on heart period, systolic arterial pressure and respira…
Detecting nonlinear causal interactions between dynamical systems by non-uniform embedding of multiple time series.
2010
This study introduces a new approach for the detection of nonlinear Granger causality between dynamical systems. The approach is based on embedding the multivariate (MV) time series measured from the systems X and Y by means of a sequential, non-uniform procedure, and on using the corrected conditional entropy (CCE) as unpredictability measure. The causal coupling from X to Y is quantified as the relative decrease of CCE measured after allowing the series of X to enter the embedding procedure for the description of Y. The ability of the approach to quantify nonlinear causality is assessed on MV time series measured from simulated dynamical systems with unidirectional coupling (the Rössler-…
Estimating the decomposition of predictive information in multivariate systems
2015
In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of co…
Reliability of Short-Term Heart Rate Variability Indexes Assessed through Photoplethysmography
2018
The gold standard method to monitor heart rate variability (HRV) comprises measuring the time series of interbeat interval durations from electrocardiographic (ECG) recordings. However, due to the widespread use, simplicity and usability of photoplethysmographic (PPG) techniques, monitoring pulse rate variability (PRV) from pulse wave recordings has become a viable alternative to standard HRV analysis. The present study investigates the accuracy of PRV, measured as a surrogate of HRV, for the quantification of descriptive indexes computed in the time domain (mean, variance), frequency domain (low-to-high frequency power ratio LF/HF, HF band central frequency) and information domain (entropy…