Unsupervised Clustering and Analysis of Contraction-Dependent Fetal Heart Rate Segments
Author:
Affiliation:
1. Stony Brook University,Department of Electrical and Computer Engineering,Stony Brook,NY,USA,11794-2350
2. Stony Brook University,Department of Obstetrics, Gynecology and Reproductive Medicine,Stony Brook,NY,USA,11794-2350
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09747598.pdf?arnumber=9747598
Reference20 articles.
1. Multimodal Convolutional Neural Networks to Detect Fetal Compromise During Labor and Delivery
2. Computer-Aided Diagnosis System of Fetal Hypoxia Incorporating Recurrence Plot With Convolutional Neural Network
3. Discovering Causalities from Cardiotocography Signals using Improved Convergent Cross Mapping with Gaussian Processes
4. Using uterine activity to improve fetal heart rate variability analysis for detection of asphyxia during labor
5. Detection rate of fetal distress using contraction-dependent fetal heart rate variability analysis
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1. A Review of AI-Based Approaches for Suspected Fetal Abnormalities;Information Systems Engineering and Management;2024
2. Cardiotocography Signal Abnormality Detection Based on Deep Semi-Unsupervised Learning;Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies;2023-12-04
3. ML-Based Interpretation of Cardiotocography Data: Current State and Future Research;2023 International Conference of Computer Science and Information Technology (ICOSNIKOM);2023-11-10
4. Approaches to Preventing Intrapartum Fetal Injury;Frontiers in Pediatrics;2022-09-23
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