Faes Luca, Nollo Giandomenico, Krohova Jana, Czippelova Barbora, Turianikova Zuzana, Javorka Michal
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:1563-1566. doi: 10.1109/EMBC.2017.8037135.
To fully elucidate the complex physiological mechanisms underlying the short-term autonomic regulation of heart period (H), systolic and diastolic arterial pressure (S, D) and respiratory (R) variability, the joint dynamics of these variables need to be explored using multivariate time series analysis. This study proposes the utilization of information-theoretic measures to measure causal interactions between nodes of the cardiovascular/cardiorespiratory network and to assess the nature (synergistic or redundant) of these directed interactions. Indexes of information transfer and information modification are extracted from the H, S, D and R series measured from healthy subjects in a resting state and during postural stress. Computations are performed in the framework of multivariate linear regression, using bootstrap techniques to assess on a single-subject basis the statistical significance of each measure and of its transitions across conditions. We find patterns of information transfer and modification which are related to specific cardiovascular and cardiorespiratory mechanisms in resting conditions and to their modification induced by the orthostatic stress.
为了充分阐明心脏周期（H）、收缩压和舒张压（S、D）以及呼吸（R）变异性短期自主调节背后的复杂生理机制，需要使用多变量时间序列分析来探索这些变量的联合动态。本研究提出利用信息论方法来测量心血管/心肺网络节点之间的因果相互作用，并评估这些定向相互作用的性质（协同或冗余）。从健康受试者在静息状态和姿势应激期间测量的H、S、D和R序列中提取信息传递和信息修改指标。计算在多变量线性回归框架内进行，使用自助法技术在单受试者基础上评估每个指标及其跨条件转换的统计显著性。我们发现了与静息状态下特定心血管和心肺机制以及体位应激引起的机制改变相关的信息传递和修改模式。
