Long-term fetal heart rate (FHR) monitoring during the antepartum period,
increasingly popularized by electronic FHR monitoring, represents a growing
approach in FHR monitoring. This kind of continuous monitoring, in contrast to
the short-term one, collects an extended period of fetal heart data. This
offers a more comprehensive understanding of fetus's conditions. However, the
interpretation of long-term antenatal fetal heart monitoring is still in its
early stages, lacking corresponding clinical standards. Furthermore, the
substantial amount of data generated by continuous monitoring imposes a
significant burden on clinical work when analyzed manually. To address above
challenges, this study develops an automatic analysis system named LARA
(Long-term Antepartum Risk Analysis system) for continuous FHR monitoring,
combining deep learning and information fusion methods. LARA's core is a
well-established convolutional neural network (CNN) model. It processes
long-term FHR data as input and generates a Risk Distribution Map (RDM) and
Risk Index (RI) as the analysis results. We evaluate LARA on inner test
dataset, the performance metrics are as follows: AUC 0.872, accuracy 0.816,
specificity 0.811, sensitivity 0.806, precision 0.271, and F1 score 0.415. In
our study, we observe that long-term FHR monitoring data with higher RI is more
likely to result in adverse outcomes (p=0.0021). In conclusion, this study
introduces LARA, the first automated analysis system for long-term FHR
monitoring, initiating the further explorations into its clinical value in the
future.

该研究开发了一个名为 LARA 的长期胎心率监测自动分析系统，结合了深度学习和信息融合方法，其核心是一个卷积神经网络模型。该系统通过处理长期胎心率数据并生成风险分布图和风险指数作为分析结果，具有良好的性能。研究观察到具有较高风险指数的长期胎心率监测数据更有可能导致不良结果。该研究首次引入了 LARA 作为长期胎心率监测的自动分析系统，并为未来其在临床价值方面开展进一步的探索奠定了基础。

基于长期产前电子胎心监测数据的胎儿健康监测的深度学习与信息融合及模型解释

Deep Learning with Information Fusion and Model Interpretation for  Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart  Rate Monitoring Data