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Information Decomposition in the Frequency Domain: a New Framework to Study Cardiovascular and Cardiorespiratory Oscillations
bioRxiv - Bioengineering Pub Date : 2020-10-15 , DOI: 10.1101/2020.10.14.338939
Luca Faes , Riccardo Pernice , Gorana Mijatovic , Yuri Antonacci , Jana Cernanova Krohova , Michal Javorka , Alberto Porta

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 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.

中文翻译:

频域中的信息分解:研究心血管和心脏呼吸振荡的新框架

尽管跨光谱和信息理论方法被广泛用于生理时间序列的多变量分析,但在文献中它们的综合利用却远远不够。这项研究为多元信息测度的频谱分解引入了一个框架,该框架为目标和两个源时间序列之间共享的信息提供了特定频率的量化,并将其扩展为与源如何以独特的方式对目标动态做出贡献有关的数量,冗余和协同的信息。在线性交互随机过程的仿真中说明了该框架,该框架显示了它如何允许检索特定频段内的过程共享的信息量,否则该信息量将无法通过时域信息度量来检测,以及光谱测量无法检测到的耦合特征。然后,将其应用于在静息仰卧位和抬头俯仰期间监测的健康受试者中测得的心脏周期,收缩压和舒张压以及呼吸变异性的时间序列。我们显示这些可变性系列共享的独特,冗余和协同信息的频谱测量,整合在特定的生理学关注频段内,反映了心血管和心肺振荡及其姿势压力引起的短期调节的机制。在仰卧休息位置和抬头倾斜过程中监测的健康受试者中测得的收缩压和舒张压以及呼吸变异性。我们显示这些可变性系列共享的独特,冗余和协同信息的频谱测量,整合在特定的生理学关注频段内,反映了心血管和心肺振荡及其姿势压力引起的短期调节的机制。在仰卧休息位置和抬头倾斜过程中监测的健康受试者中测得的收缩压和舒张压以及呼吸变异性。我们显示这些可变性系列共享的独特,冗余和协同信息的频谱测量,整合在特定的生理学关注频段内,反映了心血管和心肺振荡及其姿势压力引起的短期调节的机制。
更新日期:2020-10-17
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