Information decomposition in the frequency domain: a new framework to study cardiovascular and cardiorespiratory oscillations

While cross-spectral and information-theoretic approaches are widely used for the multivariate analysis of physiological time series, their combined utilization is far less developed in the literature. This study introduces a framework for the spectral decomposition of multivariate information measures, which provides frequency-specific quantifications of the information shared between a target and two source time series and of its expansion into amounts related to how the sources contribute to the target dynamics with unique, redundant and synergistic information. The framework is illustrated in simulations of linearly interacting stochastic processes, showing how it allows us to retrieve amounts of information shared by the processes within specific frequency bands which are otherwise not detectable by time-domain information measures, as well as coupling features which are not detectable by spectral measures. Then, it is applied to the time series of heart period, systolic and diastolic arterial pressure and respiration variability measured in healthy subjects monitored in the resting supine position and during head-up tilt. We show that the spectral measures of unique, redundant and synergistic information shared by these variability series, integrated within specific frequency bands of physiological interest and reflect the mechanisms of short-term regulation of cardiovascular and cardiorespiratory oscillations and their alterations induced by the postural stress. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

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Information Decomposition in the Frequency Domain: a New Framework to Study Cardiovascular and Cardiorespiratory Oscillations

10.1101/2020.10.14.338939 ◽

2020 ◽

Author(s):

Luca Faes ◽

Riccardo Pernice ◽

Gorana Mijatovic ◽

Yuri Antonacci ◽

Jana Cernanova Krohova ◽

...

Keyword(s):

Time Series ◽

Spectral Measures ◽

Short Term ◽

Information Theoretic ◽

Frequency Bands ◽

Information Measures ◽

Head Up Tilt ◽

Specific Frequency ◽

Domain Information ◽

New Framework

SummaryWhile cross-spectral and information-theoretic approaches are widely used for the multivariate analysis of physiological time series, their combined utilization is far less developed in the literature. This study introduces a framework for the spectral decomposition of multivariate information measures, which provides frequency-specific quantifications of the information shared between a target and two source time series and of its expansion into amounts related to how the sources contribute to the target dynamics with unique, redundant and synergistic information. The framework is illustrated in simulations of linearly interacting stochastic processes, showing how it allows to retrieve amounts of information shared by the processes within specific frequency bands which are otherwise not detectable by time-domain information measures, as well as coupling features which are not detectable by spectral measures. Then, it is applied to the time series of heart period, systolic and diastolic arterial pressure and respiration variability measured in healthy subjects monitored in the resting supine position and during head-up tilt. We show that the spectral measures of unique, redundant and synergistic information shared by these variability series, integrated within specific frequency bands of physiological interest, reflect the mechanisms of short term regulation of cardiovascular and cardiorespiratory oscillations and their alterations induced by the postural stress.

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A method for decomposing multivariate time series into a causal hierarchy within specific frequency bands

Journal of Computational Neuroscience ◽

10.1007/s10827-018-0691-y ◽

2018 ◽

Vol 45 (2) ◽

pp. 59-82 ◽

Cited By ~ 1

Author(s):

Jonathan D. Drover ◽

Nicholas D. Schiff

Keyword(s):

Time Series ◽

Multivariate Time Series ◽

Frequency Bands ◽

Specific Frequency

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Relative complexity changes in time series using information measures

Physica A Statistical Mechanics and its Applications ◽

10.1016/s0378-4371(00)00309-5 ◽

2000 ◽

Vol 286 (3-4) ◽

pp. 457-473 ◽

Cited By ~ 37

Author(s):

M.E. Torres ◽

L.G. Gamero

Keyword(s):

Time Series ◽

Relative Complexity ◽

Information Measures

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Effect of Geometric Shape of Periodic Cavities in Attenuating Baseframe Vibration

Volume 1: Acoustics, Vibration, and Phononics ◽

10.1115/imece2020-23585 ◽

2020 ◽

Author(s):

S. N. Das ◽

Kachita Kohli ◽

Ayush Kumar ◽

G. R. Sabareesh

Keyword(s):

Stop Band ◽

Rotating Machinery ◽

Vibration Level ◽

Structural Vibrations ◽

Frequency Bands ◽

Natural Modes ◽

Vibration Attenuation ◽

Specific Frequency ◽

Different Shapes ◽

Air Cavities

Abstract Vibration attenuation is an important factor while designing rotating machinery since frequency lying in the range corresponding to natural modes of structures can result in resonance and ultimately failure. Damping dissipates energy in the system, which reduces the vibration level. The mitigation of vibrations can be achieved by designing the base frame with periodic air holes. The periodicity in air holes result in vibration attenuation by providing a stop band. A finite element-based approach is developed to predict the modal and frequency response. The analysis is carried out with different shapes of periodic cavities in order to study the effectiveness of periodic stop bands in attenuating vibrations. The amount of mass removed due to the periodic cavities is kept constant. It is seen that better attenuation is obtained in case of periodic cavities compared to a uniform base frame. Among the different geometries tested, rectangular cavities showed better results than circular and square cavities. As a result, it is seen that waves propagate along periodic cells only within specific frequency bands called the “Pass bands”, while these waves are completely blocked within other frequency bands called the “Stopbands”. The air cavities filter structural vibrations in certain frequency bands resulting in effective attenuation.

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An Information Theoretic Approach for Creating 3D Spatial Images from 4D Time Series Data

Microscopy and Microanalysis ◽

10.1017/s1431927617001180 ◽

2017 ◽

Vol 23 (S1) ◽

pp. 100-101

Author(s):

Willy Wriggers ◽

Julio Kovacs ◽

Federica Castellani ◽

P. Thomas Vernier ◽

Dean J. Krusienski

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Theoretic Approach ◽

Information Theoretic ◽

Information Theoretic Approach

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Information Theoretic Optimality of Observation Driven Time Series Models

SSRN Electronic Journal ◽

10.2139/ssrn.2423765 ◽

2014 ◽

Cited By ~ 5

Author(s):

Francisco Blasques ◽

Siem Jan Koopman ◽

Andre Lucas

Keyword(s):

Time Series ◽

Time Series Models ◽

Information Theoretic

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Information Guided Exploration of Scalar Values and Isocontours in Ensemble Datasets

Entropy ◽

10.3390/e20070540 ◽

2018 ◽

Vol 20 (7) ◽

pp. 540 ◽

Cited By ~ 1

Author(s):

Subhashis Hazarika ◽

Ayan Biswas ◽

Soumya Dutta ◽

Han-Wei Shen

Keyword(s):

Weather Forecasting ◽

Conditional Entropy ◽

Specific Information ◽

Theoretic Approach ◽

Information Theoretic ◽

Information Measures ◽

New Information ◽

Real World Datasets ◽

The Individual ◽

Information Theoretic Approach

Uncertainty of scalar values in an ensemble dataset is often represented by the collection of their corresponding isocontours. Various techniques such as contour-boxplot, contour variability plot, glyphs and probabilistic marching-cubes have been proposed to analyze and visualize ensemble isocontours. All these techniques assume that a scalar value of interest is already known to the user. Not much work has been done in guiding users to select the scalar values for such uncertainty analysis. Moreover, analyzing and visualizing a large collection of ensemble isocontours for a selected scalar value has its own challenges. Interpreting the visualizations of such large collections of isocontours is also a difficult task. In this work, we propose a new information-theoretic approach towards addressing these issues. Using specific information measures that estimate the predictability and surprise of specific scalar values, we evaluate the overall uncertainty associated with all the scalar values in an ensemble system. This helps the scientist to understand the effects of uncertainty on different data features. To understand in finer details the contribution of individual members towards the uncertainty of the ensemble isocontours of a selected scalar value, we propose a conditional entropy based algorithm to quantify the individual contributions. This can help simplify analysis and visualization for systems with more members by identifying the members contributing the most towards overall uncertainty. We demonstrate the efficacy of our method by applying it on real-world datasets from material sciences, weather forecasting and ocean simulation experiments.

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Minimum Message Length in Hybrid ARMA and LSTM Model Forecasting

Entropy ◽

10.3390/e23121601 ◽

2021 ◽

Vol 23 (12) ◽

pp. 1601

Author(s):

Zheng Fang ◽

David L. Dowe ◽

Shelton Peiris ◽

Dedi Rosadi

Keyword(s):

Time Series ◽

Environmental Science ◽

Short Term Memory ◽

Arima Model ◽

Theoretic Approach ◽

Arma Models ◽

Information Theoretic ◽

Minimum Message Length ◽

Message Length ◽

Real World Datasets

Modeling and analysis of time series are important in applications including economics, engineering, environmental science and social science. Selecting the best time series model with accurate parameters in forecasting is a challenging objective for scientists and academic researchers. Hybrid models combining neural networks and traditional Autoregressive Moving Average (ARMA) models are being used to improve the accuracy of modeling and forecasting time series. Most of the existing time series models are selected by information-theoretic approaches, such as AIC, BIC, and HQ. This paper revisits a model selection technique based on Minimum Message Length (MML) and investigates its use in hybrid time series analysis. MML is a Bayesian information-theoretic approach and has been used in selecting the best ARMA model. We utilize the long short-term memory (LSTM) approach to construct a hybrid ARMA-LSTM model and show that MML performs better than AIC, BIC, and HQ in selecting the model—both in the traditional ARMA models (without LSTM) and with hybrid ARMA-LSTM models. These results held on simulated data and both real-world datasets that we considered. We also develop a simple MML ARIMA model.

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