DISCOVERING CAUSALITIES FROM CARDIOTOCOGRAPHY SIGNALS USING IMPROVED CONVERGENT CROSS MAPPING WITH GAUSSIAN PROCESSES.
DISCOVERING CAUSALITIES FROM CARDIOTOCOGRAPHY SIGNALS USING IMPROVED CONVERGENT CROSS MAPPING WITH GAUSSIAN PROCESSES.
复制标题
使用高斯过程改进的收敛交叉映射从心脏科学信号中发现因果关系。
DOI：
10.1109/icassp40776.2020.9053462
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Djurić PM
中科院分区：
文献类型：
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作者：
Feng G;Quirk JG;Djurić PM
收敛的交叉映射（CCM）是为耦合时间序列中的因果发现而设计的，在该时间序列中，Granger因果关系可能不适用，因为可分离性假设。但是，CCM对观察噪声并不强大，这将其适用性限制在已知嘈杂的信号上。此外，需要使用网格搜索方法选择状态空间重建的参数。在本文中，我们使用高斯工艺提出了一种新颖的CCM版本，以发现嘈杂时间序列的因果关系。具体而言，我们采用CCM的概念，并以原则上的方式使用非参数贝叶斯概率框架内的高斯流程执行关键步骤。首先在模拟数据上验证了所提出的方法，然后用于理解胎儿心率和子宫活性在交付前的最后两个小时的相互作用以及对产科感兴趣的相互作用。我们的结果表明，子宫活性会影响胎儿心率，这与最近的临床研究一致。
Convergent cross mapping (CCM) is designed for causal discovery in coupled time series, where Granger causality may not be applicable because of a separability assumption. However, CCM is not robust to observation noise which limits its applicability on signals that are known to be noisy. Moreover, the parameters for state space reconstruction need to be selected using grid search methods. In this paper, we propose a novel improved version of CCM using Gaussian processes for discovery of causality from noisy time series. Specifically, we adopt the concept of CCM and carry out the key steps using Gaussian processes within a non-parametric Bayesian probabilistic framework in a principled manner. The proposed approach is first validated on simulated data, and then used for understanding the interaction between fetal heart rate and uterine activity in the last two hours before delivery and of interest in obstetrics. Our results indicate that uterine activity affects the fetal heart rate, which agrees with recent clinical studies.