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Journal ArticleDOI

Measuring regularity by means of a corrected conditional entropy in sympathetic outflow.

TLDR
The reduction of complexity of the neural control obtained by spinalization decreases the regularity in the sympathetic outflow, thus pointing to a weaker coupling between the sympathetic discharge and ventilation, and the proposed index is obtained without an a-priori definition of the pattern length.
Abstract
A new method for measuring the regularity of a process over short data sequences is reported. This method is based on the definition of a new function (the corrected conditional entropy) and on the extraction of its minimum. This value is taken as an index in the information domain quantifying the regularity of the process. The corrected conditional entropy is designed to decrease in relation to the regularity of the process (like other estimates of the entropy rate), but it is able to increase when no robust statistic can be performed as a result of a limited amount of available samples. As a consequence of the minimisation procedure, the proposed index is obtained without an a-priori definition of the pattern length (i.e. of the embedding dimension of the reconstructed phase space). The method is validated on simulations and applied to beat- to-beat sequences of the sympathetic discharge obtained from decerebrate artificially ventilated cats. At control, regular, both quasiperiodic and periodic (locked to ventilation) dynamics are observed. During the sympathetic activation induced by inferior vena cava occlusion, the presence of phase-locked patterns and the increase in regularity of the sympathetic discharge evidence an augmented coupling between the sympathetic discharge and ventilation. The reduction of complexity of the neural control obtained by spinalization decreases the regularity in the sympathetic outflow, thus pointing to a weaker coupling between the sympathetic discharge and ventilation.

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Citations
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Journal ArticleDOI

Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg?

TL;DR: In this paper, the authors focus on the classification of human, bot, and cyborg accounts on Twitter and conduct a set of large-scale measurements with a collection of over 500,000 accounts.
Proceedings ArticleDOI

Who is tweeting on Twitter: human, bot, or cyborg?

TL;DR: This paper proposes a classification system that uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot or cyborg on Twitter and demonstrates the efficacy of the proposed classification system.
Journal ArticleDOI

Entropy, entropy rate, and pattern classification as tools to typify complexity in short heart period variability series

TL;DR: The proposed approach based on quantification of complexity allows a full characterization of heart period dynamics and the identification of experimental conditions known to differently perturb cardiovascular regulation.
Journal ArticleDOI

Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices.

TL;DR: The optimized sample entropy estimate and the mean heart beat interval each contributed to accurate detection of AF in as few as 12 heartbeats, and the coefficient of sample entropy (COSEn) has high degrees of accuracy in distinguishing AF from normal sinus rhythm in 12-beat calculations performed hourly.
Journal ArticleDOI

Local active information storage as a tool to understand distributed neural information processing

TL;DR: It is suggested that LAIS will be a useful quantity to test theories of cortical function, such as predictive coding, when measured on a local scale in time and space in voltage sensitive dye imaging data from area 18 of the cat.
References
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TL;DR: This chapter discusses the concept of a Random Variable, the meaning of Probability, and the axioms of probability in terms of Markov Chains and Queueing Theory.
Journal ArticleDOI

Approximate entropy as a measure of system complexity.

TL;DR: Analysis of a recently developed family of formulas and statistics, approximate entropy (ApEn), suggests that ApEn can classify complex systems, given at least 1000 data values in diverse settings that include both deterministic chaotic and stochastic processes.
Journal ArticleDOI

Geometry from a Time Series

TL;DR: In this paper, the existence of low-dimensional chaotic dynamical systems describing turbulent fluid flow was determined experimentally by reconstructing phase-space pictures from the observation of a single coordinate of any dissipative dynamical system and determining the dimensionality of the system's attractor.
Journal ArticleDOI

Spectrum analysis—A modern perspective

TL;DR: In this paper, a summary of many of the new techniques developed in the last two decades for spectrum analysis of discrete time series is presented, including classical periodogram, classical Blackman-Tukey, autoregressive (maximum entropy), moving average, autotegressive-moving average, maximum likelihood, Prony, and Pisarenko methods.
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