Assessment of granger causality by nonlinear model identification: Application to short-term cardiovascular variability
Assessment of granger causality by nonlinear model identification: Application to short-term cardiovascular variability
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DOI：
10.1007/s10439-008-9441-z
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发表时间：
2008-03-01
影响因子：
3.8
通讯作者：
Chon, Ki H.
中科院分区：
文献类型：
--
作者：
Faes, Luca;Nollo, Giandomenico;Chon, Ki H.
提出了一种基于非线性自回归（NAR）和非线性自回归外生（NARX）模型的二元时间序列格兰杰因果关系估计方法。该方法通过在包括与输入时间序列相关联的动态时量化输出时间序列的可预测性改善（PI）来评估两个时间序列之间的双边相互作用，即，从NAR到NARX预测。通过最优参数搜索（OPS）算法进行NARX模型识别，并将其结果与最小二乘法进行比较，以确定最适合用于实验数据的方法。使用替代数据技术评估PI的统计学显著性。所提出的方法进行了测试，涉及短期实现的线性随机过程和非线性确定性信号中，无论是单向或双向耦合和不同的相互作用强度的模拟例子。结果表明，基于OPS的NARX模型在检测强加的格兰杰因果关系条件方面具有较高的准确性和灵敏度。此外，基于OPS的NARX模型比最小二乘法更准确。将该方法应用于收缩压和心率变异性信号的分析，验证了该方法的可行性。特别是，我们发现了两个信号之间的双边因果关系，证明了与NAR模型预测相比，NARX模型预测的PI值显着降低，这也得到了替代数据分析的证实。此外，我们发现显着减少的复杂性的动力学的两个因果途径的两个信号的身体位置从仰卧到直立的改变。所提出的是一种通用方法，因此，它可以应用于各种各样的生理信号，以更好地理解正常和疾病状况之间可能不同的因果关系和耦合。
A method for assessing Granger causal relationships in bivariate time series, based on nonlinear autoregressive (NAR) and nonlinear autoregressive exogenous (NARX) models is presented. The method evaluates bilateral interactions between two time series by quantifying the predictability improvement (PI) of the output time series when the dynamics associated with the input time series are included, i.e., moving from NAR to NARX prediction. The NARX model identification was performed by the optimal parameter search (OPS) algorithm, and its results were compared to the least-squares method to determine the most appropriate method to be used for experimental data. The statistical significance of the PI was assessed using a surrogate data technique. The proposed method was tested with simulation examples involving short realizations of linear stochastic processes and nonlinear deterministic signals in which either unidirectional or bidirectional coupling and varying strengths of interactions were imposed. It was found that the OPS-based NARX model was accurate and sensitive in detecting imposed Granger causality conditions. In addition, the OPS-based NARX model was more accurate than the least squares method. Application to the systolic blood pressure and heart rate variability signals demonstrated the feasibility of the method. In particular, we found a bilateral causal relationship between the two signals as evidenced by the significant reduction in the PI values with the NARX model prediction compared to the NAR model prediction, which was also confirmed by the surrogate data analysis. Furthermore, we found significant reduction in the complexity of the dynamics of the two causal pathways of the two signals as the body position was changed from the supine to upright. The proposed is a general method, thus, it can be applied to a wide variety of physiological signals to better understand causality and coupling that may be different between normal and diseased conditions.