The rapid advancement of deep neural networks (DNNs) has significantly transformed
various sectors, demonstrating unparalleled proficiency in managing intricate tasks in …

Abstract Background and Objective The patterns present in biosignals, such as fetal heart
rate (FHR), are valuable indicators of fetal well-being. In designing biosignal analysis …

Objective Traditional methods face challenges in accurately analyzing fetal heart rate (FHR)
signals due to the complexity of accelerations and decelerations (Acc/Dec) and their cyclic …

Introduction Cardiotocography, which consists in monitoring the fetal heart rate as well as
uterine activity, is widely used in clinical practice to assess fetal wellbeing during labor and …

Objective According to guidelines on electronic fetal heart rate (FHR) monitoring, visual FHR
interpretation by obstetricians depends on the recognition of patterns (mainly including …

Monitoring the heart rate of a fetus is the only method for continuously monitoring fetal well-
being during labour. Decelerations in the fetal heart rate, usually corresponding with …

Machine learning techniques enable computers to acquire intelligence through learning.
Trained machines can carry out various tasks, such as prediction, classification, clustering …

Background Automated fetal heart rate (FHR) analysis removes inter-and intra-expert
variability, and is a promising solution for reducing the occurrence of fetal acidosis and the …

Background The fetal heart rate (FHR) serves as a guide to fetal well-being during the first
stage of delivery. The visual morphological analysis of the FHR during labor is subject to …

Accurate analysis of Fetal Heart Rate (FHR) signal is often impeded by challenges such as
data scarcity and label imbalance, which affect the reliability and robustness of deep …