Deep Learning with Information Fusion and Model Interpretation for
  Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart
  Rate Monitoring Data
Zenghui Lin Xintong Liu Nan Wang Ruichen Li Qingao Liu Jingying Ma Liwei Wang Yan Wang Shenda Hong
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.