ArticleJournal of clinical medicine2018
A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State.
Article in Journal of clinical medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Non-linear Methods Predominant in Fetal Heart Rate Analysis: A Systematic Review.Frontiers in medicine · 2021Pooled it
- CTG-FRAME: development and validation of a multi‑modal deep learning for intrapartum escalation support and severe fetal acidemia prediction.BMC medical informatics and decision making · 2026Article
- Review
- Hybrid-FHR: a multi-modal AI approach for automated fetal acidosis diagnosis.BMC medical informatics and decision making · 2024Article
- Computerised Cardiotocography Analysis for the Automated Detection of Fetal Compromise during Labour: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Applications of artificial intelligence in obstetrics.Ultrasonography (Seoul, Korea) · 2023Article
- A lightweight fetal distress-assisted diagnosis model based on a cross-channel interactive attention mechanism.Frontiers in physiology · 2023Article
- Deep learning based fetal distress detection from time frequency representation of cardiotocogram signal using Morse wavelet: research study.BMC medical informatics and decision making · 2022Article
- Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data.Sensors (Basel, Switzerland) · 2022Article
- Machine Learning Model for Classifying the Results of Fetal Cardiotocography Conducted in High-Risk Pregnancies.Yonsei medical journal · 2022Article
- Heart rate markers for prediction of fetal acidosis in an experimental study on fetal sheep.Scientific reports · 2022Article
- UNSUPERVISED CLUSTERING AND ANALYSIS OF CONTRACTION-DEPENDENT FETAL HEART RATE SEGMENTS.Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · 2022Article
- BOOST ENSEMBLE LEARNING FOR CLASSIFICATION OF CTG SIGNALS.Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · 2022Article
- A deep feature fusion network for fetal state assessment.Frontiers in physiology · 2022Article
- Machine Learning Algorithm to Predict Acidemia Using Electronic Fetal Monitoring Recording Parameters.Entropy (Basel, Switzerland) · 2021Article
- Investigating the interpretability of fetal status assessment using antepartum cardiotocographic records.BMC medical informatics and decision making · 2021Article
- Challenges of Developing Robust AI for Intrapartum Fetal Heart Rate Monitoring.Frontiers in artificial intelligence · 2021Review
- [Intelligent fetal state assessment based on genetic algorithm and least square support vector machine].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Continuous monitoring of the fetal heart rate (FHR) signal has been widely used to allow obstetricians to obtain detailed physiological information about newborns. However, visual interpretation of FHR traces causes inter-observer and intra-observer variability. Therefore, this study proposed a novel computerized analysis software of the FHR signal (CAS-FHR), aimed at providing medical decision support. First, to the best of our knowledge, the software extracted the most comprehensive features (47) from different domains, including morphological, time, and frequency and nonlinear domains. Then, for the intelligent assessment of fetal state, three representative machine learning algorithms (decision tree (DT), support vector machine (SVM), and adaptive boosting (AdaBoost)) were chosen to execute the classification stage. To improve the performance, feature selection/dimensionality reduction methods (statistical test (ST), area under the curve (AUC), and principal component analysis (PCA)) were designed to determine informative features. Finally, the experimental results showed that AdaBoost had stronger classification ability, and the performance of the selected feature set using ST was better than that of the original dataset with accuracies of 92% and 89%, sensitivities of 92% and 89%, specificities of 90% and 88%, and F-measures of 95% and 92%, respectively. In summary, the results proved the effectiveness of our proposed approach involving the comprehensive analysis of the FHR signal for the intelligent prediction of fetal asphyxia accurately in clinical practice.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.