Mutual information rate of nonstationary statistical signals

Miao, Hongxia; Zhang, Feng; Tao, Ran

doi:10.1016/j.sigpro.2020.107531

Cited by 8 publications

(4 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MIR of the bivariate process S = { X , Y } is defined as the limit, if it exists, of the rate at which the MI between dynamic sequences taken from X and Y increases over time ( Komaee, 2020 ; Miao et al, 2020 )

The MIR serves as a dynamic measure for the information shared per unit of time between two dynamical systems and was adopted in various forms to quantify dynamic interactions among physiological processes ( Baptista and Kurths, 2008 ; Mijatovic et al, 2021 ; Faes et al, 2022 ). This measure of dynamic coupling can be decomposed in terms of other information-theoretic measures, offering valuable insights into the dynamics of each process and the coupling relationships within the bivariate system ( Barà et al, 2023 ).…”

Section: Framework For the Surrogate Data Analysis Of Dynamic Correla...mentioning

confidence: 99%

See 1 more Smart Citation

Testing dynamic correlations and nonlinearity in bivariate time series through information measures and surrogate data analysis

Pinto,

Lazic,

Antonacci

et al. 2024

Front. Netw. Physiol.

View full text Add to dashboard Cite

The increasing availability of time series data depicting the evolution of physical system properties has prompted the development of methods focused on extracting insights into the system behavior over time, discerning whether it stems from deterministic or stochastic dynamical systems. Surrogate data testing plays a crucial role in this process by facilitating robust statistical assessments. This ensures that the observed results are not mere occurrences by chance, but genuinely reflect the inherent characteristics of the underlying system. The initial process involves formulating a null hypothesis, which is tested using surrogate data in cases where assumptions about the underlying distributions are absent. A discriminating statistic is then computed for both the original data and each surrogate data set. Significantly deviating values between the original data and the surrogate data ensemble lead to the rejection of the null hypothesis. In this work, we present various surrogate methods designed to assess specific statistical properties in random processes. Specifically, we introduce methods for evaluating the presence of autodependencies and nonlinear dynamics within individual processes, using Information Storage as a discriminating statistic. Additionally, methods are introduced for detecting coupling and nonlinearities in bivariate processes, employing the Mutual Information Rate for this purpose. The surrogate methods introduced are first tested through simulations involving univariate and bivariate processes exhibiting both linear and nonlinear dynamics. Then, they are applied to physiological time series of Heart Period (RR intervals) and respiratory flow (RESP) variability measured during spontaneous and paced breathing. Simulations demonstrated that the proposed methods effectively identify essential dynamical features of stochastic systems. The real data application showed that paced breathing, at low breathing rate, increases the predictability of the individual dynamics of RR and RESP and dampens nonlinearity in their coupled dynamics.

show abstract

Section: Framework For the Surrogate Data Analysis Of Dynamic Correla...mentioning

confidence: 99%

“…The MIR of the bivariate process S = {X, Y} is defined as the limit, if it exists, of the rate at which the MI between dynamic sequences taken from X and Y increases over time (Komaee, 2020;Miao et al, 2020)…”

Section: Mutual Information Ratementioning

confidence: 99%

Testing dynamic correlations and nonlinearity in bivariate time series through information measures and surrogate data analysis

Pinto,

Lazic,

Antonacci

et al. 2024

Front. Netw. Physiol.

View full text Add to dashboard Cite

show abstract

“…In this study, we utilized mutual information [20] , [21] , [22] as an indication for general correlation (relevance) between a pair of genomic features, and mathematically integrated it with the number of colocalizations between the features to define a score for maximal colocalization minimal correlation (MACMIC). The MACMIC score allows us to quantitatively prioritize the feature combinations that have large number of colocalizations but low correlation.…”

Section: Introductionmentioning

confidence: 99%

MACMIC Reveals a Dual Role of CTCF in Epigenetic Regulation of Cell Identity Genes

Wang

Xia

Zhou³

et al. 2021

Genomics, Proteomics &Amp; Bioinformatics

View full text Add to dashboard Cite

Numerous studies of relationship between epigenomic features have focused on their strong correlation across the genome, likely because such relationship can be easily identified by many established methods for correlation analysis. However, two features with little correlation may still colocalize at many genomic sites to implement important functions. There is no bioinformatic tool for researchers to specifically identify such feature pairs. Here, we develop a method to identify feature pairs in which two features have maximal colocalization minimal correlation (MACMIC) across the genome. By MACMIC analysis of 3306 feature pairs in 16 human cell types, we reveal a dual role of CCCTC-binding factor (CTCF) in epigenetic regulation of cell identity genes. Although super-enhancers are associated with activation of target genes, only a subset of super-enhancers colocalized with CTCF regulate cell identity genes. At super-enhancers colocalized with CTCF, CTCF is required for the active marker H3K27ac in cell types requiring the activation, and also required for the repressive marker H3K27me3 in other cell types requiring repression. Our work demonstrates the biological utility of the MACMIC analysis and reveals a key role for CTCF in epigenetic regulation of cell identity. The code for MACMIC is available at https://github.com/bxia888/MACMIC .

show abstract

“…the instantaneous spectrum of the signal changes over time. Such signals, called as time-varying or nonstationary signals, are ubiquitous and commonly encountered in various nature and manmade systems, namely communication, radar and mechanical engineering (Miao et al , 2020; Arab et al , 2018; Alstrom et al , 2018). For a variety of tasks ranging from signal processing to further application, effective representation for such signals in an appropriate transform domain is an important and challenging issue, since time or frequency descriptions typically do not offer comprehensive details about essential characteristics.…”

Section: Introductionmentioning

confidence: 99%

High-concentration time–frequency analysis for multi-component nonstationary signals based on combined multi-window Gabor transform

Wang

Meng

Wang

2021

View full text Add to dashboard Cite

PurposeThis study aims to reveal the essential characteristics of nonstationary signals and explore the high-concentration representation in the joint time–frequency (TF) plane.Design/methodology/approachIn this paper, the authors consider the effective TF analysis for nonstationary signals consisting of multiple components.FindingsTo make it, the authors propose the combined multi-window Gabor transform (CMGT) under the scheme of multi-window Gabor transform by introducing the combination operator. The authors establish the completeness utilizing the discrete piecewise Zak transform and provide the perfect-reconstruction conditions with respect to combined TF coefficients. The high-concentration is achieved by optimization. The authors establish the optimization function with considerations of TF concentration and computational complexity. Based on Bergman formulation, the iteration process is further analyzed to obtain the optimal solution.Originality/valueWith numerical experiments, it is verified that the proposed CMGT performs better in TF analysis for multi-component nonstationary signals.

show abstract

Mutual information rate of nonstationary statistical signals

Cited by 8 publications

References 43 publications

Testing dynamic correlations and nonlinearity in bivariate time series through information measures and surrogate data analysis

Testing dynamic correlations and nonlinearity in bivariate time series through information measures and surrogate data analysis

MACMIC Reveals a Dual Role of CTCF in Epigenetic Regulation of Cell Identity Genes

High-concentration time–frequency analysis for multi-component nonstationary signals based on combined multi-window Gabor transform

Contact Info

Product

Resources

About