Mijatovic Gorana, Antonacci Yuri, Faes Luca
Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:341-344. doi: 10.1109/EMBC46164.2021.9629688.
We present the implementation to cardiovascular variability of a method for the information-theoretic estimation of the directed interactions between event-based data. The method allows to compute the transfer entropy rate (TER) from a source to a target point process in continuous time, thus overcoming the severe limitations associated with time discretization of event-based processes. In this work, the method is evaluated on coupled cardiovascular point processes representing the heartbeat dynamics and the related peripheral pulsation, first using a physiologically-based simulation model and then studying real point-process data from healthy subjects monitored at rest and during postural stress. Our results document the ability of TER to detect direction and strength of the interactions between cardiovascular processes, also highlighting physiologically plausible interaction mechanisms.
我们展示了一种基于信息论的方法在心血管变异性方面的应用，该方法用于估计基于事件的数据之间的定向相互作用。该方法能够在连续时间内计算从源点过程到目标点过程的转移熵率（TER），从而克服了与基于事件的过程时间离散化相关的严重局限性。在这项工作中，首先使用基于生理学的模拟模型，然后研究来自健康受试者在静息和姿势应激状态下监测到的真实点过程数据，对该方法在代表心跳动力学和相关外周搏动的耦合心血管点过程上进行评估。我们的结果证明了TER能够检测心血管过程之间相互作用的方向和强度，同时也突出了生理上合理的相互作用机制。
