Yu Kezi, Quirk J Gerald, Djurić Petar M
Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794, USA.
Department of Obstetrics/Gynecology, Stony Brook University Hospital, Stony Brook University, Stony Brook, NY 11794, USA.
Proc IEEE Int Conf Acoust Speech Signal Process. 2017 Mar;2017:876-880. doi: 10.1109/icassp.2017.7952281. Epub 2017 Jun 19.
In this paper, we propose an application of non-parametric Bayesian (NPB) models to classification of fetal heart rate recordings. More specifically, the models are used to discriminate between fetal heart rate recordings that belong to fetuses that may have adverse asphyxia outcomes and those that are considered normal. In our work we rely on models based on hierarchical Dirichlet processes. Two mixture models were inferred from recordings that represent healthy and unhealthy fetuses, respectively. The models were then used to classify new recordings. We compared the classification performance of the NPB models with that of support vector machines on real data and concluded that the NPB models achieved better performance.
在本文中，我们提出将非参数贝叶斯（NPB）模型应用于胎儿心率记录的分类。更具体地说，这些模型用于区分可能有不良窒息结局的胎儿的心率记录和被认为正常的胎儿的心率记录。在我们的工作中，我们依赖基于分层狄利克雷过程的模型。分别从代表健康和不健康胎儿的记录中推断出两个混合模型。然后将这些模型用于对新记录进行分类。我们在真实数据上比较了NPB模型和支持向量机的分类性能，得出NPB模型具有更好性能的结论。
