Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:4590-4594. doi: 10.1109/EMBC48229.2022.9871253.
Prenatal fetal monitoring, which can monitor the growth and health of the fetus, is vital for pregnant women before delivery. During pregnancy, it is essential to classify whether the fetus is abnormal, which helps physicians carry out early intervention to avoid fetal heart hypoxia and even death. Fetal heart rate and uterine contraction signals obtained by fetal heart monitoring equipment are essential to estimate fetal health status. In this paper, we pre-process the obtained data set and enhance them using Hermite interpolation on the abnormal classification in the samples. We use the 1D-CNN and GRU hybrid models to extract the abstract features of fetal heart rate and uterine contraction signals. Several evaluation metrics are used for evaluation, and the accuracy is 96 %, while the sensitivity is 95 %, and the specificity is 96 %. The experiments show the effectiveness of the proposed method, which can provide physicians and users with more stable, efficient, and convenient diagnosis and decision support.
产前胎儿监测可以监测胎儿的生长和健康状况，对于分娩前的孕妇至关重要。在怀孕期间，对胎儿是否异常进行分类至关重要，这有助于医生进行早期干预，避免胎儿心脏缺氧甚至死亡。胎儿心率和子宫收缩信号是通过胎儿心率监测设备获得的，对于评估胎儿健康状况至关重要。在本文中，我们对获得的数据集进行预处理，并使用 Hermite 插值对样本中的异常分类进行增强。我们使用 1D-CNN 和 GRU 混合模型提取胎儿心率和子宫收缩信号的抽象特征。使用了几个评估指标进行评估，准确率为 96%，灵敏度为 95%，特异性为 96%。实验表明，所提出的方法是有效的，它可以为医生和用户提供更稳定、高效、方便的诊断和决策支持。
