Feng Guanchao, Quirk J Gerald, Djurić Petar M
Department of Electrical and Computer Engineering, Stony Brook University.
Department of Obstetrics/Gynecology, Stony Brook University Hospital Stony Brook, NY 11794.
Int Workshop Comput Adv Multisens Adapt Process. 2019 Dec;2019:381-385. doi: 10.1109/CAMSAP45676.2019.9022670. Epub 2020 Mar 5.
During labor, fetal heart rate (FHR) and uterine activity (UA) are continuously monitored with Cardiotocography (CTG). The FHR and UA signals are visually inspected by obstetricians to assess the fetal well-being. However, due to the subjectivity of the visual inspection, the evaluations of CTG recordings performed by obstetricians have high inter- and intra-variability. The computerized analysis of FHR relies on features either hand-crafted by experts or automatically learned by machine learning methods. However, the popular interpretable FHR features, in general, have low correlation with the pH value of the umbilical cord blood at birth, which is the current gold standard for labeling FHRs in the computerized analysis of FHRs. The features found by machine learning methods, by contrast, usually have limited interpretability. In this paper, in a follow up of our previous work on FHR analysis using Gaussian processes (GPs), we explore the possibility of using the hyperparameters of GPs as interpretable features. Our results indicate that some GP features achieve high correlation with the pH values, while at the same time they are not highly correlated with other popular features.
在分娩过程中，使用胎心监护仪（CTG）持续监测胎儿心率（FHR）和子宫活动（UA）。产科医生通过目视检查FHR和UA信号来评估胎儿的健康状况。然而，由于目视检查的主观性，产科医生对CTG记录的评估在不同医生之间以及同一医生不同时间之间存在很大差异。FHR的计算机化分析依赖于专家手工制作的特征或通过机器学习方法自动学习的特征。然而，一般来说，流行的可解释FHR特征与出生时脐带血pH值的相关性较低，而脐带血pH值是目前FHR计算机化分析中标记FHR的金标准。相比之下，通过机器学习方法发现的特征通常可解释性有限。在本文中，作为我们之前使用高斯过程（GPs）进行FHR分析工作的后续研究，我们探索了将GPs的超参数用作可解释特征的可能性。我们的结果表明，一些GP特征与pH值具有高度相关性，同时它们与其他流行特征的相关性并不高。
