ReviewSensors (Basel, Switzerland)2021
A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Postnatal Steroids in Preterm Infants: A Narrative Review Series-Part 2: Cardiovascular Impacts.Children (Basel, Switzerland) · 2026Review
- Predicting Intrapartum Acidemia: A Review of Approaches Based on Fetal Heart Rate.Bioengineering (Basel, Switzerland) · 2026Review
- Machine learning-based fetal health prediction and development of smart web application.Frontiers in artificial intelligence · 2026Article
- Outcome of Preterm Neonates > 32 Weeks Gestation in Relation to Three-Tiered Fetal Heart Rate Categorization.Medicina (Kaunas, Lithuania) · 2025Article
- Machine Learning Models as Early Warning Systems for Neonatal Infection.Clinics in perinatology · 2025Review
- Advanced Predictive Analytics for Fetal Heart Rate Variability Using Digital Twin Integration.Sensors (Basel, Switzerland) · 2025Article
- Development of autonomic innervation at the venous pole of the heart: bridging the gap from mice to human.Journal of translational medicine · 2025Article
- Day-to-day dynamics of fetal heart rate variability to detect chorioamnionitis in preterm premature rupture of membranes.PloS one · 2025Article
- Cross-Database Evaluation of Deep Learning Methods for Intrapartum Cardiotocography Classification.IEEE journal of translational engineering in health and medicine · 2025Article
- Machine Learning for Biomedical Applications.Bioengineering (Basel, Switzerland) · 2024Article
- Article
- Rapid detection of fetal compromise using input length invariant deep learning on fetal heart rate signals.Scientific reports · 2024Article
- Fetal Heart Rate Preprocessing Techniques: A Scoping Review.Bioengineering (Basel, Switzerland) · 2024Article
- Fetal heart rate spectral analysis in raw signals and PRSA-derived curve: normal and pathological fetuses discrimination.Medical & biological engineering & computing · 2024Article
- Analysis of fetal heart rate fluctuations in women diagnosed with preeclampsia during the latent phase of labor.Frontiers in physiology · 2024Article
- Classification and regression model to manage the hospitalization for laparoscopic cholecystectomy.Scientific reports · 2023Article
- Conventional CardiotocographyMaedica · 2023Article
- Computerised Cardiotocography Analysis for the Automated Detection of Fetal Compromise during Labour: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Predictive Analysis of Hospital Stay after Caesarean Section: A Single-Center Study.Bioengineering (Basel, Switzerland) · 2023Article
- Detection of Suspicious Cardiotocographic Recordings by Means of a Machine Learning Classifier.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors.
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