Permutation entropy: One concept, two approaches

Since C. Bandt and B. Pompe introduced permutation entropy in 2002 for piecewise strictly monotonous self-maps of one-dimensional intervals, this concept has been generalized to ever more general settings by means of two similar, though not equivalent, approaches. The first one keeps the original spirit in that it uses “sharp” dynamics and the corresponding ordinal partitions. The second uses symbolic (or “coarse-grained” dynamics with respect to arbitrary finite partitions, as in the conventional approach to the Kolmogorov-Sinai entropy of dynamical systems. Precisely, one of the main questions along these two avenues refers to the relation between permutation entropy and Kolmogorov-Sinai entropy. In this paper the authors will explain the underpinnings of both approaches and the latest theoretical results on permutation entropy. The authors also discuss some remaining open questions.     

How does a choice of Markov partition affect the resultant symbolic dynamics?

The mutual relationship among Markov partitions is investigated for one-dimensional piecewise monotonic map. It is shown that if a Markov partition is regarded as a map-refinement of the other Markov partition, that is, a concept we newly introduce in this article, one can uniquely translate a set of symbolic sequences by one Markov partition to those by the other or vice versa. However, the set of symbolic sequences constructed using Markov partitions is not necessarily translated with each other if there exists no map-refinement relation among them. By using a roof map we demonstrate how the resultant symbolic sequences depend on the choice of Markov partitions.     

Time-series analysis of sleep-wake stage of rat EEG using time-dependent pattern entropy

We performed electroencephalography (EEG) for six male Wistar rats to clarify temporal behaviors at different levels of consciousness. Levels were identified both by conventional sleep analysis methods and by our novel entropy method. In our method, time-dependent pattern entropy is introduced, by which EEG is reduced to binary symbolic dynamics and the pattern of symbols in a sliding temporal window is considered. A high correlation was obtained between level of consciousness as measured by the conventional method and mean entropy in our entropy method. Mean entropy was maximal while awake (stage W) and decreased as sleep deepened. These results suggest that time-dependent pattern entropy may offer a promising method for future sleep research.     

Estimating good discrete partitions from observed data: symbolic false nearest neighbors

A symbolic analysis of observed time series requires a discrete partition of a continuous state space containing the dynamics. A particular kind of partition, called "generating," preserves all deterministic dynamical information in the symbolic representation, but such partitions are not obvious beyond one dimension. Existing methods to find them require significant knowledge of the dynamical evolution operator. We introduce a statistic and algorithm to refine empirical partitions for symbolic state reconstruction. This method optimizes an essential property of a generating partition, avoiding topological degeneracies, by minimizing the number of "symbolic false nearest neighbors." It requires only the observed time series and is sensible even in the presence of noise when no truly generating partition is possible.     

The application of the transfer entropy to gappy time series

The application of the transfer entropy to gappy symbolic time series is discussed. Although the transfer entropy can fail to correctly identify the drive-response relationship, it is able to robustly detect phase relationships. Hence, it might still be of use in applications requiring the detection of changes in these relationships.     

Estimation of entropies and dimensions by nonlinear symbolic time series analysis

Symbolic nonlinear time series analysis methods have the potential for analyzing nonlinear data efficiently with low sensitivity to noise. In symbolic nonlinear time series analysis a time series for a fixed delay is partitioned into a small number (called the alphabet size) of cells labeled by symbols, creating a symbolic time series. Symbolic methods involve computing the statistics of words made from the symbolic time series. Specifically, the Shannon entropy of the distribution of possible words for a range of word lengths is computed. The rate of increase of the entropy with word length is the metric (Kolmogorov-Sinai) entropy. Methods of computing the metric entropy for flows as well as for maps are shown. A method of computing the information dimension appropriate to symbolic analysis is proposed. In terms of this formulation, the information dimension is determined by the scaling of entropy as alphabet size is modestly increased, using the information obtained from large word length. We discuss the role of sampling time and the issue of using these methods when there may be no generating partition.     

Entropy production, fractals, and relaxation to equilibrium

The theory of entropy production in nonequilibrium, Hamiltonian systems, previously described for steady states using partitions of phase space, is here extended to time dependent systems relaxing to equilibrium. We illustrate the main ideas by using a simple multibaker model, with some nonequilibrium initial state, and we study its progress toward equilibrium. The central results are (i) the entropy production is governed by an underlying, exponentially decaying fractal structure in phase space, (ii) the rate of entropy production is largely independent of the scale of resolution used in the partitions, and (iii) the rate of entropy production is in agreement with the predictions of nonequilibrium thermodynamics.     

A generalized Kolmogorov-Sinai-like entropy under Markov shifts in symbolic dynamics

In this paper, a generalized Kolmogorov-Sinai-like entropy ( entropy) in the sense of Tsallis is proposed with a nonextensive parameter q under Markov shifts, which contains the classical Kolmogorov-Sinai (KS) entropy and the Rényi entropy as well as Bernoulli shifts as special cases. To verify the formula of this entropy, a one-dimensional iterative system is chosen as an example of Markov shifts, and its entropy is evaluated by a new refinement method of symbolic dynamics called symbolic refinement which differs from the conventional numerical method. The numerical results show that this entropy is monotonically decreasing as q increases.     

The Entropy of Partitions on MV-Algebras

Partitions of MV-algebras are studied. Using the notion of a state (as a probabilisticmeasure) on MV-algebras, we introduce the entropy of partitions. We show asuitable method for the refinement of partitions and the subadditivity of theentropy with respect to this refinement.     

Symbolic dynamics of ventricular tachycardia and ventricular fibrillation

In this paper, the symbolic dynamics analysis was used to analyze the complexity of normal heartbeat signal (NSR), Ventricular tachycardia (VT) and ventricular fibrillation (VF) signals. By calculating the information entropy value of symbolic sequences, the complexities were quantified. Based on different information entropy values, NSR, VT and VF signals were distinguished with satisfactory results. The study showed that a sudden drop of symbolic sequence’s entropy value indicated that the patients most likely entered the episode of ventricular tachycardia and this was a crucial episode for the clinical treatment of patients. It had important clinical significance for the automatic diagnosis.     

Numerical and experimental investigation of the effect of filtering on chaotic symbolic dynamics

Motivated by the practical consideration of the measurement of chaotic signals in experiments or the transmission of these signals through a physical medium, we investigate the effect of filtering on chaotic symbolic dynamics. We focus on the linear, time-invariant filters that are used frequently in many applications, and on the two quantities characterizing chaotic symbolic dynamics: topological entropy and bit-error rate. Theoretical consideration suggests that the topological entropy is invariant under filtering. Since computation of this entropy requires that the generating partition for defining the symbolic dynamics be known, in practical situations the computed entropy may change as a filtering parameter is changed. We find, through numerical computations and experiments with a chaotic electronic circuit, that with reasonable care the computed or measured entropy values can be preserved for a wide range of the filtering parameter.     

Estimating generating partitions of chaotic systems by unstable periodic orbits

An outstanding problem in chaotic dynamics is to specify generating partitions for symbolic dynamics in dimensions larger than 1. It has been known that the infinite number of unstable periodic orbits embedded in the chaotic invariant set provides sufficient information for estimating the generating partition. Here we present a general, dimension-independent, and efficient approach for this task based on optimizing a set of proximity functions defined with respect to periodic orbits. Our algorithm allows us to obtain the approximate location of the generating partition for the Ikeda-Hammel-Jones-Moloney map.     

基于符号动力学的认知事件相关电位的复杂度分析

引入了符号动力学方法分析认知事件相关电位(ERP)的复杂度.以混合模型生成的随机时间序列为例,对近似熵和符号熵作了比较.应用符号熵分析了Oddball范式中不同任务条件(靶刺激和非靶刺激)下的ERP的复杂度.研究发现,额区、中央区和顶区的ERP复杂度在刺激呈现后的任务加工时间段内显著减小(非靶刺激和靶刺激分别在刺激呈现后200—300和400—500ms),而且靶刺激ERP复杂度大约在P300成分的峰值时刻达到最小值,在响应之后逐渐回升.这表明基于符号动力学的复杂度分析能够反映认知任务加工的时间过程,并且 关键词： 事件相关电位 符号动力学 熵     

Conditional Entropy of Partitions on Quantum Logic

A construction of conditional entropy of partitions on quantum logic is given, and the properties of conditional entropy are investigated.     

Conditional Entropy of Partitions on Quantum Logic

A construction of conditional entropy of partitions on quantum logic is given, and the properties of conditional entropy are investigated.     

Globally enumerating unstable periodic orbits for observed data using symbolic dynamics

The unstable periodic orbits of a chaotic system provide an important skeleton of the dynamics in a chaotic system, but they can be difficult to find from an observed time series. We present a global method for finding periodic orbits based on their symbolic dynamics, which is made possible by several recent methods to find good partitions for symbolic dynamics from observed time series. The symbolic dynamics are approximated by a Markov chain estimated from the sequence using information-theoretical concepts. The chain has a probabilistic graph representation, and the cycles of the graph may be exhaustively enumerated with a classical deterministic algorithm, providing a global, comprehensive list of symbolic names for its periodic orbits. Once the symbolic codes of the periodic orbits are found, the partition is used to localize the orbits back in the original state space. Using the periodic orbits found, we can estimate several quantities of the attractor such as the Lyapunov exponent and topological entropy.     

An approach to comparing Kolmogorov-Sinai and permutation entropy

In this paper we discuss the relationship between permutation entropy and Kolmogorov-Sinai entropy in the one-dimensional case. For this, we consider partitions of the state space of a dynamical system using ordinal patterns of order (d + n? 1) on the one hand, and using n-letter words of ordinal patterns of order d on the other hand. The answer to the question of how different these partitions are provides an approach to comparing the entropies.