Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility Study

Bipolar Disorder (BD) is an illness with high prevalence and a huge social and economic impact. It is recurrent, with a long-term evolution in most cases. Early treatment and continuous monitoring have proven to be very effective in mitigating the causes and consequences of BD. However, no tools are currently available for a massive and semi-automatic BD patient monitoring and control. Taking advantage of recent technological developments in the field of wearables, this paper studies the feasibility of a BD episodes classification analysis while using entropy measures, an approach successfully applied in a myriad of other physiological frameworks. This is a very difficult task, since actigraphy records are highly non-stationary and corrupted with artifacts (no activity). The method devised uses a preprocessing stage to extract epochs of activity, and then applies a quantification measure, Slope Entropy, recently proposed, which outperforms the most common entropy measures used in biomedical time series. The results confirm the feasibility of the approach proposed, since the three states that are involved in BD, depression, mania, and remission, can be significantly distinguished.     

The effect of time delay on Approximate & Sample Entropy calculations

Approximate and Sample Entropy are two widely used techniques to measure system complexity or regularity based on chosen parameters such as pattern length, m, and tolerance, r. In this paper, we investigate how different values of the time delay parameter, τ can be used in conjunction with standard values of m and r in the computation of Approximate and Sample Entropy. The results show that for time series generated by nonlinear dynamics that have long range correlation, a time delay equal to the first zero crossing or minimum of the autocorrelation function can provide additional information into the characteristics of the time series that may be useful in comparative analysis. With a unity delay, we demonstrate that Approximate and Sample Entropy are possibly measuring only the (linear) autocorrelation properties of the signal, and these are highly invariant under surrogate data generation methods. Hence when this occurs, the complexity measures of the surrogate and original data are not statistically different.     

Entropy generation in radiative motion of tangent hyperbolic nanofluid in presence of activation energy and nonlinear mixed convection

In this communication, an optimization of entropy generation is performed through thermodynamics second law. Tangent hyperbolic nanomaterial model is used which describes the important slip mechanism namely Brownian and thermophoresis diffusions. MHD fluid is considered. The novel binary chemical reaction model is implemented to characterize the impact of activation energy. Nonlinear mixed convection, dissipation and Joule heating are considered. Appropriate similarity transformations are implemented to get the required coupled ODEs system. The obtained system is tackled for series solutions by homotopy method. Graphs are constructed to analyze the impact of different flow parameters on entropy number, nanoparticle volume concentration, temperature and velocity fields. Total entropy generation rate is calculated via various flow variables. It is noticed from obtained results that entropy number depend up thermal irreversibility, viscous dissipation and Joule heating irreversibility and concentration irreversibility. Decreasing behavior of concentration is witnessed for higher estimations of chemical reaction variable. Entropy number is more for higher Hartmann number, Weissenberg number and chemical reaction variable while contrast behavior is noted for Bejan number.     

Approximate Entropy in Canonical and Non-Canonical Fiction

Computational textual aesthetics aims at studying observable differences between aesthetic categories of text. We use Approximate Entropy to measure the (un)predictability in two aesthetic text categories, i.e., canonical fiction (‘classics’) and non-canonical fiction (with lower prestige). Approximate Entropy is determined for series derived from sentence-length values and the distribution of part-of-speech-tags in windows of texts. For comparison, we also include a sample of non-fictional texts. Moreover, we use Shannon Entropy to estimate degrees of (un)predictability due to frequency distributions in the entire text. Our results show that the Approximate Entropy values can better differentiate canonical from non-canonical texts compared with Shannon Entropy, which is not true for the classification of fictional vs. expository prose. Canonical and non-canonical texts thus differ in sequential structure, while inter-genre differences are a matter of the overall distribution of local frequencies. We conclude that canonical fictional texts exhibit a higher degree of (sequential) unpredictability compared with non-canonical texts, corresponding to the popular assumption that they are more ‘demanding’ and ‘richer’. In using Approximate Entropy, we propose a new method for text classification in the context of computational textual aesthetics.     

From physics to economics: An econometric example using maximum relative entropy

Econophysics, is based on the premise that some ideas and methods from physics can be applied to economic situations. We intend to show in this paper how a physics concept such as entropy can be applied to an economic problem. In so doing, we demonstrate how information in the form of observable data and moment constraints are introduced into the method of Maximum relative Entropy (MrE). A general example of updating with data and moments is shown. Two specific econometric examples are solved in detail which can then be used as templates for real world problems. A numerical example is compared to a large deviation solution which illustrates some of the advantages of the MrE method.     

Entropy of Hidden Markov Processes via Cycle Expansion

Hidden Markov Processes (HMP) is one of the basic tools of the modern probabilistic modeling. The characterization of their entropy remains however an open problem. Here the entropy of HMP is calculated via the cycle expansion of the zeta-function, a method adopted from the theory of dynamical systems. For a class of HMP this method produces exact results both for the entropy and the moment-generating function. The latter allows to estimate, via the Chernoff bound, the probabilities of large deviations for the HMP. More generally, the method offers a representation of the moment-generating function and of the entropy via convergent series.     

Entropy correlation distance method. The Euro introduction effect on the Consumer Price Index

The idea of entropy was introduced in thermodynamics, but it can be used in time series analysis. There are various ways to define and measure the entropy of a system. Here the so called Theil index, which is often used in economy and finance, is applied as it were an entropy measure. In this study the time series are remapped through the Theil index. Then the linear correlation coefficient between the remapped time series is evaluated as a function of time and time window size and the corresponding statistical distance is defined. The results are compared with the the usual correlation distance measure for the time series themselves. As an example this entropy correlation distance method (ECDM) is applied to several series, as those of the Consumer Price Index (CPI) in order to test some so called globalisation processes. Distance matrices are calculated in order to construct two network structures which are next analysed. The role of two different time scales introduced by the Theil index and a correlation coefficient is also discussed. The evolution of the mean distance between the most developed countries is presented and the globalisation periods of the prices discussed. It is finally shown that the evolution of mean distance between the most developed countries on several networks follows the process of introducing the European currency — the Euro. It is contrasted to the GDP based analysis. It is stressed that the entropy correlation distance measure is more suitable in detecting significant changes, like a globalisation process than the usual statistical (correlation based) measure.     

Verification of numerical simulation method for entropy generation of radiation heat transfer in semitransparent medium

The numerical simulation method of radiative entropy generation in participating media presented by Caldas and Semiao [Entropy generation through radiative transfer in participating media: analysis and numerical computation. JQSRT 2005;96:423-37] is extended to analyze the radiative entropy generation in the enclosures filled with semitransparent media. A discrete ordinates method is used to solve radiative transfer equation and radiative entropy generation. Two different examples are employed to verify the numerical simulation method of radiative entropy generation in the enclosure. Numerical results of dimensionless radiative entropy generation of enclosure are identical to that of entire thermodynamics analysis for the enclosure system. This numerical simulation method can be used in the entropy generation analysis of high-temperature systems such as boilers and furnaces, in which radiation is the dominant mode of heat transfer.     

Maximum or minimum entropy generation for open systems?

Entropy generation is analysed and obtained from the entropy balance for open systems, considering the lifetime of the natural real process. The Lagrangian approach is discussed in order to develop an analytical method to obtain the stationary states of the open irreversible systems. The stationary conditions of the open systems are obtained in relation to the entropy generation and its maximum principle. An analysis of both minimum and maximum entropy generation is proposed, suggesting that they are two different viewpoints of the same aspect: the first is related to the system, while the second is related to the interaction between the system and the environment.     

An Evaluation of Entropy Measures for Microphone Identification

Research findings have shown that microphones can be uniquely identified by audio recordings since physical features of the microphone components leave repeatable and distinguishable traces on the audio stream. This property can be exploited in security applications to perform the identification of a mobile phone through the built-in microphone. The problem is to determine an accurate but also efficient representation of the physical characteristics, which is not known a priori. Usually there is a trade-off between the identification accuracy and the time requested to perform the classification. Various approaches have been used in literature to deal with it, ranging from the application of handcrafted statistical features to the recent application of deep learning techniques. This paper evaluates the application of different entropy measures (Shannon Entropy, Permutation Entropy, Dispersion Entropy, Approximate Entropy, Sample Entropy, and Fuzzy Entropy) and their suitability for microphone classification. The analysis is validated against an experimental dataset of built-in microphones of 34 mobile phones, stimulated by three different audio signals. The findings show that selected entropy measures can provide a very high identification accuracy in comparison to other statistical features and that they can be robust against the presence of noise. This paper performs an extensive analysis based on filter features selection methods to identify the most discriminating entropy measures and the related hyper-parameters (e.g., embedding dimension). Results on the trade-off between accuracy and classification time are also presented.     

The Systematic Bias of Entropy Calculation in the Multi-Scale Entropy Algorithm

Entropy indicates irregularity or randomness of a dynamic system. Over the decades, entropy calculated at different scales of the system through subsampling or coarse graining has been used as a surrogate measure of system complexity. One popular multi-scale entropy analysis is the multi-scale sample entropy (MSE), which calculates entropy through the sample entropy (SampEn) formula at each time scale. SampEn is defined by the “logarithmic likelihood” that a small section (within a window of a length m) of the data “matches” with other sections will still “match” the others if the section window length increases by one. “Match” is defined by a threshold of r times standard deviation of the entire time series. A problem of current MSE algorithm is that SampEn calculations at different scales are based on the same matching threshold defined by the original time series but data standard deviation actually changes with the subsampling scales. Using a fixed threshold will automatically introduce systematic bias to the calculation results. The purpose of this paper is to mathematically present this systematic bias and to provide methods for correcting it. Our work will help the large MSE user community avoiding introducing the bias to their multi-scale SampEn calculation results.     

Correlations and variability in electrical signals related to earthquake activity

Evaluating complex fluctuations in geoelectric time series is an important task not only for earthquake prediction but also for understanding complex processes related to earthquake preparation. Previous studies have reported alterations, such as the emergence of correlated dynamics in geoelectric potentials prior to an important earthquake (EQ). However, the presence of correlations and its relation with variability has not been widely explored. In this work we apply the detrended fluctuation analysis and the multiscale entropy methods to analyze the fluctuations of geoelectric time series monitored in two sites located in Mexico. We systematically calculate the correlation exponents and the sample entropy (S_E) of geoelectric time series. Important differences in the scaling exponents and entropy profiles for several time scales are observed. In particular, a complex behavior, characterized by a high entropy across several scales and crossover in the correlation exponents, is observed in the vicinity of an that occurred on Sept. 14, 1995. Moreover, we compare the changes in the entropy of the original data with their corresponding shuffled version to see whether correlations in the original data are related to variability.     

Entropy production per site in (nonreversible) spin-flip processes

Entropy production per site in a (nonreversible) spin-flip process is studied. We give it a useful expression, from which a property stronger than affinity of the entropy production per site follows. Furthermore, quasi-invariance of nonequilibrium measures in the spin-flip processes is discussed via entropy production.     

A study of the time distribution of inter-cluster earthquakes in Taiwan

In this study, we analyze the time distribution of inter-cluster earthquakes in Taiwan to infer correlation of earthquake occurrences. Our study based on the Diffusion Entropy Analysis (DEA) shows that there is positive correlation when the event time series is sampled by a time unit ranging from 120 to 1800 min. Also in the range of the time unit, two-slope feature in the determination of scaling exponent in DEA is observed, and the scenario is demonstrated by the simulations of synthetic earthquake sequence generated by the Copying Mistake Map model. Our simulation suggests that the earthquake sequence may consist of a randomness sequence and a correlated sequence, and the latter contributes to the correlation observed in our analysis.     

吸收式制冷循环系统的热力学分析

从热力学观点讨论了工作温度对于制冷循环系统性能的影响。分析了与循环时间有关的温度效率和熵产数。对于一个相对较短的循环时间,吸收/解吸收热量转换器的温度效率在200秒后可以达到92%。熵产数Ns由在一个循环系统内生成的不可逆性参数和热量转换器流体有效性参数之间的比率决定。结果显示,在使用一个30℃冷源的情况下高级吸收式循环系统的熵产数Ns在热水温度是45℃至55℃之间时是相对较小的,而对于传统循环,在使用相同冷源温度的情况下,热水温度在65℃到75℃之间时,Ns是相对较小的。     

Heat capacity and entropy of two electrons quantum dot in a magnetic field with parabolic interaction

The thermodynamic properties of two electrons in two dimensional parabolic GaAs quantum dot are studied where both the magnetic field and the e–e interaction are fully considered. The e–e interaction has been treated by a model potential which makes the Hamiltonian exactly solvable. The energy spectrum is used to calculate the canonical partition function, and then we obtain the thermodynamic properties; mean energy, heat capacity and entropy as a function of temperature (T) and magnetic field (B).A steep transition from zero to 4k_B is observed in the heat capacity as a function of temperature for small values of magnetic field and saturates within a small temperature range, also the heat capacity has a peak-like structure at low temperature, while for high magnetic field heat capacity develops a shoulder at 2k_B then it approaches the saturation value with further increase in temperature. The entropy increases with increasing temperature, but at higher temperature, it remains almost independent of the magnetic field. It is shown that, at low magnetic field values, the effect of magnetic field on heat capacity is tangible and it attains a constant value with further increase in magnetic field. Entropy is almost linearly proportional with increasing magnetic field strength.     

基于有限穿越水平可视图的短时睡眠心率变异性研究

心率数据是最易于获取的人体生理数据之一,基于心率变异性的睡眠分析是近年来各种用于日常健康管理的可穿戴设备功能的一个重要发展方向,需要不断探索可以应用于标准睡眠分期时间窗(约30 s)的各类短时特征参数.利用近期报道的有限穿越水平可视图,并进一步提出一种加权有限穿越水平可视图,将不同睡眠状态下的短时心率变异序列映射为网络,进而提取平均集聚系数、特征路径长度、集聚系数熵、路径分布熵、加权集聚系数熵和加权路径分布熵等网络特征参数进行统计分析.结果表明,各网络参数值在醒觉、浅睡期、深睡期和快速眼动期的幅度水平具有显著差异,体现了所述方法在基于短时心率变异数据的睡眠分期中的有效性.同时,进一步研究了健康年轻人和中老年人在不同睡眠状态下的网络参数值,发现两者虽然存在整体的水平差异,但是在不同睡眠状态间的变化仍具有相同的趋势,反映出相对于正常的年龄老化,睡眠调制对心脏动力学系统具有更显著的影响,也说明所述方法可作为基于心率变异性的睡眠研究的一种新的辅助工具.     

Entropy balance in distributed reversible Gray-Scott model

A practical way to calculate the entropy change in the distributed media composed of reversible Gray-Scott model is demonstrated. The entropy change is given as the sum of the entropy production and the divergence of entropy flow. The divergence of entropy is calculated based on the chemical potential of steady state. It becomes evident that: (i) the entropy change for the emergence of dissipative structures in the open system can be positive or negative, (ii) most of the entropy produced inside the system is thrown out to the environment when dissipative structures are developing, (iii) the entropy production and the divergence of entropy flow balance completely, when the system shows static steady states, (iv) the entropy change behaves as if it is the time derivative of the entropy production. Prior to these calculations of entropy balance, the features of emergent patterns in the two-dimensional system are examined in terms of entropy production solely. The results imply that the entropy production can be an index for us to discriminate spatial patterns, but is not a global thermodynamic potential for the evolution of dissipative structures.     

基于移动网格的熵稳定格式

提出一种基于移动网格的熵稳定格式求解双曲型守恒律方程.该方法利用等分布原理得到新的网格分布,基于守恒型插值公式计算新的网格上的物理量,使用熵稳定数值通量和三阶强稳定Runge-Kutta时间推进方法得到下一时刻的数值解.数值算例表明该格式不仅能有效提高解在间断处的分辨率,而且能消除可能产生的伪振荡.     

Long memory and volatility clustering: Is the empirical evidence consistent across stock markets?

Long memory and volatility clustering are two stylized facts frequently related to financial markets. Traditionally, these phenomena have been studied based on conditionally heteroscedastic models like ARCH, GARCH, IGARCH and FIGARCH, inter alia. One advantage of these models is their ability to capture nonlinear dynamics. Another interesting manner to study the volatility phenomenon is by using measures based on the concept of entropy. In this paper we investigate the long memory and volatility clustering for the SP 500, NASDAQ 100 and Stoxx 50 indexes in order to compare the US and European Markets. Additionally, we compare the results from conditionally heteroscedastic models with those from the entropy measures. In the latter, we examine Shannon entropy, Renyi entropy and Tsallis entropy. The results corroborate the previous evidence of nonlinear dynamics in the time series considered.