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首页> 外文期刊>Biomedical Engineering: Applications, Basis and Communications >Multivariate time-varying autoregressive modeling of fetal sympatho-vagal balance through gestation
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Multivariate time-varying autoregressive modeling of fetal sympatho-vagal balance through gestation

机译：妊娠期胎儿交感迷走平衡的多元时变自回归模型

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摘要

A processing framework is proposed to model relative changes in fetal sympatho-vagal balance at equally spaced gestational periods. The proposed method is based on a multivariable time-varying autoregression (TVAR) of the beat-to-beat time differences obtained from non-invasive fetal electrocardiographic (ECG) or magnetocardiographic (MCG) measurements. In order to quantify the sympatho-vagal balance at each measured gestational period, the ratio between the standard deviation of normal-to-normal (SDNN) beat intervals and the sum of absolute differences (SAD) is computed. While the SDNN quantifies the overall variability of the sympathetic and vagal systems, the SAD enhances short-term variability components related to vagal control, then the ratio of these two compares with high specificity the overall variability against the short-term vagal component in the time domain. The SDNN/SAD ratio is used to form a new data set by removing short-term variability events, then leaving only those corresponding to longer-term sympatho-vagal balance. The new data set is then analyzed as a dynamical system by fitting it to a suitable multivariate TVAR, and relative changes in the sympatho-vagal balance through the analyzed gestational periods are assumed to be related to the dynamics of the time-varying coefficients of the TVAR. In order to demonstrate the applicability of the proposed method, simulated and real fetal E/MCG data are analyzed. The results show that the modeling approach is able to infer the expected trend seen through sympatho-vagal development.
机译：提出了一个处理框架来模拟在等间隔的妊娠期胎儿交感迷走神经平衡的相对变化。所提出的方法基于从无创胎儿心电图（ECG）或心电图（MCG）测量获得的心跳时间差的多变量时变自回归（TVAR）。为了量化在每个测得的妊娠期的交感迷走神经平衡，计算了正常对正常拍打间隔的标准偏差（SDNN）与绝对差总和（SAD）之间的比率。尽管SDNN量化了交感神经系统和迷走神经系统的总体变异性，但SAD增强了与迷走神经控制相关的短期变异性成分，然后这两种比率的高特异性比较了当时的短期迷走神经成分的整体变异性域。 SDNN / SAD比率用于通过删除短期变异性事件，然后仅保留与长期交感迷走神经平衡相对应的事件，来形成新的数据集。然后，通过将新数据集拟合到合适的多元TVAR，将其作为动力系统进行分析，并假设通过分析的妊娠期交感迷走平衡的相对变化与运动时变系数的动力学有关。 TVAR。为了证明该方法的适用性，分析了模拟的和实际的胎儿E / MCG数据。结果表明，该建模方法能够推断通过交感迷走发展看到的预期趋势。

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