Complexity-entropy Plane Research Articles

Multifractal Properties of Human Chromosome Sequences

The intricacy and fractal properties of human DNA sequences are examined in this work. The core of this study is to discern whether complete DNA sequences present distinct complexity and fractal attributes compared with sequences containing exclusively exon regions. In this regard, the entire base pair sequences of DNA are extracted from the NCBI (National Center for Biotechnology Information) database. In order to create a time series representation for the base pair sequence {G,C,T,A}, we use the Chaos Game Representation (CGR) approach and a mapping rule f, which enables us to apply the metric known as the Complexity–Entropy Plane (CEP) and multifractal detrended fluctuation analysis (MF-DFA). To carry out our investigation, we divided human DNA into two groups: the first is composed of the 24 chromosomes, which comprises all the base pairs that form the DNA sequence, and another group that also includes the 24 chromosomes, but the DNA sequences rely only on the exons’ presence. The results show that both sets provide fractal patterns in their structure, as obtained by the CGR approach. Complete DNA sequences show a sharper visual fractal pattern than sequences composed only of exons. Moreover, the sequences occupy distinct areas of the complexity–entropy plane, and the complete DNA sequences lead to greater statistical complexity and lower entropy than the exon sequences. Also, we observed that different fractal parameters between chromosomes indicate diversity in genomic sequences. All these results occur in different scales for all chromosomes.

Multifractal analysis of maize and soybean DNA

This paper investigates the complexity of DNA sequences in maize and soybean using the multifractal detrended fluctuation analysis (MF-DFA) method, chaos game representation (CGR), and the complexity-entropy plane approach. The study aims to understand the patterns and structures of these DNA sequences, which can provide insights into their genetic makeup and improve crop yield and quality. The results show that maize and soybean DNA sequences exhibit fractal properties, indicating a complex and self-organizing structure. We observe the persistence trend between sequences of base pairs, which indicates long-range correlations between base pairs. We also identified the stochastic nature of the DNA sequences of both species.

Modelling the eye movements of dyslexic children during reading as a continuous time random walk.

The study of eye movements during reading is considered a valuable tool for understanding the underlying cognitive processes and for its ability to detect alterations that could be associated with neurocognitive deficiencies or visual conditions. During reading, the gaze moves from one position to the next on the text performing a saccade-fixation sequence. This dynamics resembles processes usually described as continuous time random walk, where the jumps are the saccadic movements and waiting times are the duration of fixations. The time between jumps (intersaccadic time) consists of stochastic waiting time and flight time, which is a function of the jump length (the amplitude of the saccade). This motivates the present proposal of a model of eye movements during reading in the framework of the intermittent random walk but considering the time between jumps as a combined stochastic-deterministic process. The parameters used in this model were obtained from records of eye movements of children with dyslexia and typically developed for children performing a reading task. The jump lengths arise from the characteristics of the selected text. The time required for the flights was obtained based on a previously proposed model. Synthetic signals were generated and compared with actual eye movement signals in a complexity-entropy plane.

Ordinal pattern-based complexity analysis of high-dimensional chaotic time series.

The ordinal pattern-based complexity-entropy plane is a popular tool in nonlinear dynamics for distinguishing stochastic signals (noise) from deterministic chaos. Its performance, however, has mainly been demonstrated for time series from low-dimensional discrete or continuous dynamical systems. In order to evaluate the usefulness and power of the complexity-entropy (CE) plane approach for data representing high-dimensional chaotic dynamics, we applied this method to time series generated by the Lorenz-96 system, the generalized Hénon map, the Mackey-Glass equation, the Kuramoto-Sivashinsky equation, and to phase-randomized surrogates of these data. We find that both the high-dimensional deterministic time series and the stochastic surrogate data may be located in the same region of the complexity-entropy plane, and their representations show very similar behavior with varying lag and pattern lengths. Therefore, the classification of these data by means of their position in the CE plane can be challenging or even misleading, while surrogate data tests based on (entropy, complexity) yield significant results in most cases.

Data Sampling Algorithm Based on Complexity-Entropy Plane for Smart Sensing Applications

This work proposes a data sampling algorithm for smart cities applications based on sensor network infrastructure. Our algorithm identifies the sensor data behavior through the Causality Complexity-Entropy Plane and performs data reduction by removing redundant data without losing the system’s properties. For this, we recognize the systems’ dynamic changes in real-time through a delimiter, named Maximum Complexity Point (MCP). Thus, we determine when to update the sampling period to maximize the system’s information content, i.e., the statistical complexity quantifier. To confirm the sampling adaptability, we apply our method in three different chaotic attractors: Rossler, Lorenz, and ${B}_{7}$ . We compared our solution with two other sampling algorithms: (i) random histogram-based sampling and the ${L}$ algorithm. We use the K-S test, the average Data Error, and the Causality Complexity-Entropy Plane to compare the results. Using our sampling approach, we observed K-S test distances less than 3% for chaotic maps and 1% for natural environments data. The best results were in the Data Error, showing an average error rate up to 13.4% lower when evaluating chaotic data and 15.7% lower when evaluating natural environments. Regarding the dispersion of points in the Causality Complexity-Entropy Plane, the sampled time-series reached regions of higher statistical complexity, indicating that they preserved information content, hence the original data’s dynamics.

Using complexity–entropy planes to detect Parkinson’s disease from short segments of haemodynamic signals

Objective. There is emerging evidence that analysing the entropy and complexity of biomedical signals can detect underlying changes in physiology which may be reflective of disease pathology. This approach can be used even when only short recordings of biomedical signals are available. This study aimed to determine whether entropy and complexity measures can detect differences between subjects with Parkinsons disease and healthy controls (HCs). Approach. A method based on a diagram of entropy versus complexity, named complexity–entropy plane, was used to re-analyse a dataset of cerebral haemodynamic signals from subjects with Parkinsons disease and HCs obtained under poikilocapnic conditions. A probability distribution for a set of ordinal patterns, designed to capture regularities in a time series, was computed from each signal under analysis. Four types of entropy and ten types of complexity measures were estimated from these distributions. Mean values of entropy and complexity were compared and their classification power was assessed by evaluating the best linear separator on the corresponding complexity–entropy planes. Main results. Few linear separators obtained significantly better classification, evaluated as the area under the receiver operating characteristic curve, than signal mean values. However, significant differences in both entropy and complexity were detected between the groups of participants. Significance. Measures of entropy and complexity were able to detect differences between healthy volunteers and subjects with Parkinson's disease, in poikilocapnic conditions, even though only short recordings were available for analysis. Further work is needed to refine this promising approach, and to help understand the findings in the context of specific pathophysiological changes.

Complexity and Entropy in Legal Language

We study the language of legal codes from different countries and legal traditions, using concepts from physics, algorithmic complexity theory and information theory. We show that vocabulary entropy, which measures the diversity of the author’s choice of words, in combination with the compression factor, which is derived from a lossless compression algorithm and measures the redundancy present in a text, is well suited for separating different writing styles in different languages, in particular also legal language. We show that different types of (legal) text, e.g. acts, regulations or literature, are located in distinct regions of the complexity-entropy plane, spanned by the information and complexity measure. This two-dimensional approach already gives new insights into the drafting style and structure of statutory texts and complements other methods.

Ordpy: A Python package for data analysis with permutation entropy and ordinal network methods.

Since Bandt and Pompe's seminal work, permutation entropy has been used in several applications and is now an essential tool for time series analysis. Beyond becoming a popular and successful technique, permutation entropy inspired a framework for mapping time series into symbolic sequences that triggered the development of many other tools, including an approach for creating networks from time series known as ordinal networks. Despite increasing popularity, the computational development of these methods is fragmented, and there were still no efforts focusing on creating a unified software package. Here, we present ordpy (http://github.com/arthurpessa/ordpy), a simple and open-source Python module that implements permutation entropy and several of the principal methods related to Bandt and Pompe's framework to analyze time series and two-dimensional data. In particular, ordpy implements permutation entropy, Tsallis and Rényi permutation entropies, complexity-entropy plane, complexity-entropy curves, missing ordinal patterns, ordinal networks, and missing ordinal transitions for one-dimensional (time series) and two-dimensional (images) data as well as their multiscale generalizations. We review some theoretical aspects of these tools and illustrate the use of ordpy by replicating several literature results.

Measuring time series based on multiscale dispersion Lempel–Ziv complexity and dispersion entropy plane

Abstract In this paper, we propose dispersion Lempel–Ziv complexity and combine it with dispersion entropy to construct complexity-entropy plane. This measure aims to identify time series with different properties. These two quantities take advantage of nonlinear symbolic representation and quantify the complexity of series. In addition, they are relatively stable for different parameters and robust against white noise with different levels. The complexity-entropy plane is able to identify nonlinear chaotic maps and distinguish them from stochastic process. Also, the multiscale features of signals are detected, suggesting the underlying dynamics. This technique is effective to track the dynamical changes of heart rate time series and to characterize different pathologic states. It can also prove that aging and disease correspond to the loss of complexity.

Generalized entropy plane based on permutation entropy and distribution entropy analysis for complex time series

Entropy is an accessible way to work as a measure of the irregularity and the uncertainty between the predicting knowledge and the given time series. Statistical complexity measure (SCM) combining Shannon entropy and the extensive Jensen–Shannon divergence provides important additional information regarding the peculiarities of the underlying probability distribution, not already detected by the entropy. In this paper, we extend the traditional complexity-entropy causality plane, which applies the diagram of SCM versus normalized Shannon entropy, to two generalized complexity-entropy plane based on Permutation entropy (PE) and Permuted distribution entropy (PEDisEn). Moreover, as the important extension of the Shannon entropy, the Tsallis entropy and Rényi entropy are used to construct the plane. We discuss the parameter selection for the PE plane and PEDisEn plane respectively. Outlier detection is recently a heated point focusing on discovering patterns that occur infrequently in the time series in data mining. However, there exists few entropy plane based methods in outlier detection. We apply the proposed procedure to the real world data for outlier detection. It turns out that the generalized entropy plane is robust to the type of original series and is efficient for detecting outliers.

Mapping and discrimination of networks in the complexity-entropy plane.

Complex networks are usually characterized in terms of their topological, spatial, or information-theoretic properties and combinations of the associated metrics are used to discriminate networks into different classes or categories. However, even with the present variety of characteristics at hand it still remains a subject of current research to appropriately quantify a network's complexity and correspondingly discriminate between different types of complex networks, like infrastructure or social networks, on such a basis. Here we explore the possibility to classify complex networks by means of a statistical complexity measure that has formerly been successfully applied to distinguish different types of chaotic and stochastic time series. It is composed of a network's averaged per-node entropic measure characterizing the network's information content and the associated Jenson-Shannon divergence as a measure of disequilibrium. We study 29 real-world networks and show that networks of the same category tend to cluster in distinct areas of the resulting complexity-entropy plane. We demonstrate that within our framework, connectome networks exhibit among the highest complexity while, e.g., transportation and infrastructure networks display significantly lower values. Furthermore, we demonstrate the utility of our framework by applying it to families of random scale-free and Watts-Strogatz model networks. We then show in a second application that the proposed framework is useful to objectively construct threshold-based networks, such as functional climate networks or recurrence networks, by choosing the threshold such that the statistical network complexity is maximized.

Detrended fluctuation analysis and entropy-complexity causality analysis of temperatures in an urbanized mountain stream

We present the results of applying detrended fluctuation analysis (DFA) to study correlations in mountain stream temperatures over a 9-year period for impacted and unimpacted conditions. Permutation entropy and complexity are also computed for stream temperature time series at various locations characterized by varying degrees of human impact, and a graphical representation of the “complexity-entropy causality plane” is used to examine the degree of disturbance caused by human activities. Urban development such as canopy removal and urban infrastructure can lead to a higher degree of anti-persistent correlations indicated by lower values of DFA exponents. This effect is more pronounced downstream due to cumulative urbanization effects with downstream distance. Seasonal variations also influence stream temperature dynamics: less anti-persistent behavior is observed in winter months, and stronger anti-persistent correlations during summer. The position in the complexity-entropy plane, reflecting distribution of short term temperature patterns, is also affected, where most sites experience a shift towards higher entropy values after restoration, and only for one site entropy is reduced. Our results indicate the important role urbanization plays on stream evolution greatly influenced by human activities. This information may provide valuable insights for watershed management and water resources management.

Chaotic edge density fluctuations in the Alcator C-Mod tokamak

Analysis of the time series obtained with the O-Mode reflectometer [Rhodes et al., 1997 Plasma Phys. Controlled Fusion 40, 493–510 (1998)] and the gas puff imaging [Cziegler et al., Phys. Plasmas 17(5), 056120 (2010)] systems on the Alcator C-Mod tokamak reveals that the turbulent edge density fluctuations are chaotic. Supporting evidence for this conclusion includes the observation of exponential power spectra (which is associated with Lorentzian-shaped pulses in the time series), the population of the corresponding Bandt-Pompe probability distributions [Bandt and Pompe, Phys. Rev. Lett. 88, 174102 (2002)], and the location of the signal on the complexity-entropy plane (C-H plane) [Rosso et al., Phys. Rev. Lett. 99, 154102 (2007)]. The classification of edge turbulence as chaotic opens the door for further work to understand the underlying process and the impact on turbulent transport.

Characterization of vehicle behavior with information theory

This work proposes the use of Information Theory for the characterization of vehicles behavior through their velocities. Three public data sets were used: i.Mobile Century data set collected on Highway I-880, near Union City, California; ii.Borl\"ange GPS data set collected in the Swedish city of Borl\"ange; and iii.Beijing taxicabs data set collected in Beijing, China, where each vehicle speed is stored as a time series. The Bandt-Pompe methodology combined with the Complexity-Entropy plane were used to identify different regimes and behaviors. The global velocity is compatible with a correlated noise with f^{-k} Power Spectrum with k >= 0. With this we identify traffic behaviors as, for instance, random velocities (k aprox. 0) when there is congestion, and more correlated velocities (k aprox. 3) in the presence of free traffic flow.

Chaotic density fluctuations in L-mode plasmas of the DIII-D tokamak

Analysis of the time series obtained with the Doppler backscattering system (Hillsheim et al 2009 Rev. Sci. Instrum. 80 0835070) in the DIII-D tokamak (Luxon 2005 Fusion Sci. Technol. 48 828) shows that intermediate wave number plasma density fluctuations in low confinement (L-mode) tokamak plasmas are chaotic. The supporting evidence is based on the shape of the power spectrum; the location of the signal in the complexity-entropy plane (C-H plane) (Rosso et al 2007 Phys. Rev. Lett. 99 154102); and the population of the corresponding Bandt–Pompe (Bandt and Pompe 2002 Phys. Rev. Lett. 88 174102) probability distributions.