ArticleFrontiers in physiology2022
A deep feature fusion network for fetal state assessment.
Article in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 26 citations in OpenAlex.
- MIRF-Net: A Multimodal Data Fusion Framework for Intrapartum Fetal Risk Assessment.Bioengineering (Basel, Switzerland) · 2026Article
- Predicting Intrapartum Acidemia: A Review of Approaches Based on Fetal Heart Rate.Bioengineering (Basel, Switzerland) · 2026Review
- Automated interpretation of cardiotocography using deep learning in a nationwide multicenter study.Scientific reports · 2025Article
- PatchCTG: A Patch Cardiotocography Transformer for Antepartum Fetal Health Monitoring.Sensors (Basel, Switzerland) · 2025Article
- Comparison and verification of detection accuracy for late deceleration with and without uterine contractions signals using convolutional neural networks.Frontiers in physiology · 2025Article
- Time-Dependent Association Between Cardiotocographic Features and Hypoxic-Ischemic Encephalopathy.IEEE access : practical innovations, open solutions · 2025Article
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- Editorial: New technologies improve maternal and newborn safety.Frontiers in medical technology · 2024Article
- Editorial: Diagnosis, monitoring, and treatment of heart rhythm: new insights and novel computational methods.Frontiers in physiology · 2023Article
- A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system.Digital healthArticle
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
CTG (cardiotocography) has consistently been used to diagnose fetal hypoxia. It is susceptible to identifying the average fetal acid-base balance but lacks specificity in recognizing prenatal acidosis and neurological impairment. CTG plays a vital role in intrapartum fetal state assessment, which can prevent severe organ damage if fetal hypoxia is detected earlier. In this paper, we propose a novel deep feature fusion network (DFFN) for fetal state assessment. First, we extract spatial and temporal information from the fetal heart rate (FHR) signal using a multiscale CNN-BiLSTM network, increasing the features' diversity. Second, the multiscale CNN-BiLSM network and frequently used features are integrated into the deep learning model. The proposed DFFN model combines different features to improve classification accuracy. The multiscale convolutional kernels can identify specific essential information and consider signal's temporal information. The proposed method achieves 61.97%, 73.82%, and 66.93% of sensitivity, specificity, and quality index, respectively, on the public CTU-UHB database. The proposed method achieves the highest QI on the private database, verifying the proposed method's effectiveness and generalization. The proposed DFFN combines the advantages of feature engineering and deep learning models and achieves competitive accuracy in fetal state assessment compared with related works.
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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.