Most of the existing studies on improvement entropy are based theory single entropy, ignoring relationship between one and another. Inspired by synergistic bubble (BE) permutation (PE), which has been pointed out previous authors, this paper aims to explore dispersion entropy. Since outperforms in many aspects, it provides better stability enhances computational efficiency We also speculate tha...

Electroencephalography (EEG) is considered the output of a brain and it is a bioelectrical signal with multiscale and nonlinear properties. Motor Imagery EEG (MI-EEG) not only has a close correlation with the human imagination and movement intention but also contains a large amount of physiological or disease information. As a result, it has been fully studied in the field of rehabilitation. To...

In the case of ergodicity much of the structure of a one-dimensional time-discrete dynamical system is already determined by its ordinal structure. In order to explain this phenomenon more precisely, we show that ordinal patterns, which describe the up and down in the orbits of an ergodic Borel measurable map on a subset of the real numbers, are in a certain sense generating. On this base we gi...

Consider a network of unreliable links, modelling for example a communication network. Estimating the reliability of the network – expressed as the probability that certain nodes in the network are connected – is a computationally difficult task. In this paper we study how the Cross-Entropy method can be used to obtain more efficient network reliability estimation procedures. Three techniques o...

Ordinal symbolic dynamics is a promising new approach to the investigation of time series and the systems behind them, with the important properties of being conceptually simple and relatively robust with respect to noise. The idea behind it is to consider the order relation between the values of a time series instead of the values themselves. Roughly speaking, a given time series is transforme...

Different techniques originated in information theory and tools from nonlinear systems theory have been applied to the analysis of electro-physiological time series. Several clinically relevant results have emerged from the use of concepts, such as entropy, chaos and complexity, in analyzing electrocardiograms and electroencephalographic (EEG) records. In this work, we develop a method based on...

Despite its prominent contribution to the free energy of solvated macromolecules such as proteins or DNA, and although principally contained within molecular dynamics simulations, the entropy of the solvation shell is inaccessible to straightforward application of established entropy estimation methods. The complication is twofold. First, the configurational space density of such systems is too...

Estimating the causal interaction between neurons is very important for better understanding the functional connectivity in neuronal networks. We propose a method called normalized permutation transfer entropy (NPTE) to evaluate the temporal causal interaction between spike trains, which quantifies the fraction of ordinal information in a neuron that has presented in another one. The performanc...

The inference of Shannon entropy out sample histograms is known to be affected by systematic and random errors that depend on the finite size available data set. This dependence was mostly investigated in multinomial case, which states are visited an independent fashion. In this paper asymptotic behavior distribution entropy, also referred as plug-in estimator, case underlying Markov process ch...