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Accounting for Respiration is Necessary to Reliably Infer Granger Causality From Cardiovascular Variability Series

机译:要从心血管变异性系列可靠地推断格兰杰因果关系,必须考虑呼吸作用

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摘要

This study was designed to demonstrate the need of accounting for respiration (R) when causality between heart period (HP) and systolic arterial pressure (SAP) is under scrutiny. Simulations generated according to a bivariate autoregressive closed-loop model were utilized to assess how causality changes as a function of the model parameters. An exogenous (X) signal was added to the bivariate autoregressive closed-loop model to evaluate the bias on causality induced when the X source was disregarded. Causality was assessed in the time domain according to a predictability improvement approach (i.e., Granger causality). HP and SAP variability series were recorded with R in 19 healthy subjects during spontaneous and controlled breathing at 10, 15, and 20 breaths/min. Simulations proved the importance of accounting for X signals. During spontaneous breathing, assessing causality without taking into consideration R leads to a significantly larger percentage of closed-loop interactions and a smaller fraction of unidirectional causality from HP to SAP. This finding was confirmed during paced breathing and it was independent of the breathing rate. These results suggest that the role of baroreflex cannot be correctly assessed without accounting for R.
机译:这项研究旨在证明,在对心脏周期(HP)和收缩期动脉压(SAP)之间的因果关系进行仔细检查时,需要考虑呼吸(R)。根据双变量自回归闭环模型生成的仿真用于评估因果关系如何随模型参数变化。将外源(X)信号添加到双变量自回归闭环模型,以评估忽略X源时引起的因果关系偏差。根据可预测性改进方法在时域中评估因果关系(即Granger因果关系)。在以10、15和20次呼吸/分钟的速度自主呼吸和控制呼吸期间,在19位健康受试者中记录了R的HP和SAP变异性系列。仿真证明了考虑X信号的重要性。在自发呼吸期间,在不考虑R的情况下评估因果关系会导致闭环交互作用的百分比显着提高,并且从HP到SAP的单向因果关系所占的比例也较小。该发现在有规律的呼吸过程中得到了证实,并且与呼吸频率无关。这些结果表明,如果不考虑R,就不能正确评估压力反射的作用。

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