Fully Automatic Classification of Cardiotocographic Signals with 1D-CNN and Bi-directional GRU
Author:
Affiliation:
1. College of Big Data and Internet, Shenzhen Technology University,Shenzhen,China
2. School of Computing Science, University of Glasgow,Glasgow,UK
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9870821/9870822/09871253.pdf?arnumber=9871253
Reference26 articles.
1. Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines
2. Classification of caesarean section and normal vaginal deliveries using foetal heart rate signals and advanced machine learning algorithms
3. Machine learning ensemble modelling to classify caesarean section and vaginal delivery types using Cardiotocography traces
4. Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques
5. Classifying the type of delivery from cardiotocographic signals: A machine learning approach
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1. Extracting fetal heart signals from Doppler using semi-supervised convolutional neural networks;Frontiers in Physiology;2024-07-08
2. Fully automatic quantification for hand synovitis in rheumatoid arthritis using pixel-classification-based segmentation network in DCE-MRI;Japanese Journal of Radiology;2024-05-24
3. A CNN-RNN unified framework for intrapartum cardiotocograph classification;Computer Methods and Programs in Biomedicine;2023-02
4. Deep Learning for Cardiotocography Analysis: Challenges and Promising Advances;Lecture Notes in Computer Science;2023
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