Entropy, as it relates to dynamical systems, is the rate of information production. Methods for estimation of the entropy of a system represented by a time series are not, however, well suited to analysis of the short and noisy data sets encountered in cardiovascular and other biological studies. Pincus introduced approximate entropy (ApEn), a set of measures of system complexity closely relate...

High throughput experiments, characteristic of studies in systems biology, produce large output data sets often at different time points or under a variety of related conditions or for different patients. In several recent papers the data is modeled by using a distribution of maximal information-theoretic entropy. We pose the question: ‘whose entropy’ meaning how do we select the variables whos...

This paper presents a novel method in driving distraction analysis: entropy analysis of ECG signals. ECG signals were recorded continuously while 15 drivers were driving with a simulator. Mental computation task was employed as driving distraction. Sample entropy and power spectrum entropy of drivers. ECG signals while they were driving with and without distraction were derived. The result indi...

The order-q Tsallis (Hq ) and Rényi entropy (Kq ) receive broad applications in the statistical analysis of complex phenomena. A generic problem arises, however, when these entropies need to be estimated from observed data. The finite size of data sets can lead to serious systematic and statistical errors in numerical estimates. In this paper, we focus upon the problem of estimating generalized...

Based on the non-standard analysis by Robinson, a new definition of the entropy of general distributions is given as a straightforward extension of Shannon's discrete entropy. The newly defined entropy has natural properties such as positiveness and invariance under transformations unlike Shannon's continuous entropy. In the light of this new definition, the meaning of Shannon's continuous entr...

HIGHLIGHTS ► Twelve entropy indices were systematically compared in monitoring depth of anesthesia and detecting burst suppression.► Renyi permutation entropy performed best in tracking EEG changes associated with different anesthesia states.► Approximate Entropy and Sample Entropy performed best in detecting burst suppression. OBJECTIVE Entropy algorithms have been widely used in analyzing E...