NeuroFetalNet: Advancing Remote Electronic Fetal Monitoring with a New Dataset and Comparative Analysis of FHR and UCP Impact

EasyChair Preprint 13557

Abstract

This paper explores the evolution of electronic fetal monitoring (EFM) and introduces remote electronic fetal monitoring (REFM) as a practical approach. A new REFM dataset is constructed, addressing gaps in existing datasets. The study compares the impact of fetal heart rate (FHR), uterine contraction pressure (UCP), and their simultaneous presence on prediction accuracy. By leveraging deep learning techniques, our study introduces NeuroFetalNet, a model that has shown superior performance in remote electronic fetal monitoring. The results emphasize the potential of advanced algorithms, specifically the multi-scale feature extractor in NeuroFetalNet, to improve the accuracy and efficiency of remote fetal monitoring. These findings have significant implications for enhancing maternal and fetal healthcare, opening up new possibilities for improved monitoring and care.

Keyphrases: Classification, REFM, ResNet, dataset, multi-scale feature extraction

@booklet{EasyChair:13557,
  author    = {Black Sun and Jiaqi Zhao and Xinrong Miao and Yanqiao Wu and Min Fang},
  title     = {NeuroFetalNet: Advancing Remote Electronic Fetal  Monitoring with a New Dataset and Comparative  Analysis of FHR and UCP Impact},
  howpublished = {EasyChair Preprint 13557},
  year      = {EasyChair, 2024}}