Machine Learning-based Detection of In-Utero Fetal Presentation from Non-Invasive Fetal ECG-Reference-Cited by-同舟云学术

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Machine Learning-based Detection of In-Utero Fetal Presentation from Non-Invasive Fetal ECG

Published:2022-09-27 Issue: Volume: Page:
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Container-title:2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
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Author:

Jaeger Katharina M.¹,Nissen Michael¹,Richer Robert¹,Rahm Simone¹,Titzmann Adriana¹,Fasching Peter A.²,Eskofier Bjoern M.¹,Leutheuser Heike¹

Affiliation:

1. Friedrich-Alexander-Universitat Erlangen-Niirnberg(FAU),Machine Learning and Data Analytics Lab (MaD Lab),Department Artificial Intelligence in Biomedical Engineering (AIBE),Erlangen,Germany

2. Erlangen University Hospital,Department of Gynecology and Obstetrics,Erlangen,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9926747/9926739/09926804.pdf?arnumber=9926804

Reference22 articles.

1. A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography

2. Personalized Anatomic Modeling for Noninvasive Fetal ECG: Methodology and Applications

3. A systematic review of cardiac time intervals utilising non-invasive fetal electrocardiogram in normal fetuses

4. The standardized 12-lead fetal electrocardiogram of the healthy fetus in mid-pregnancy: A cross-sectional study

5. Fetal movement detection based on QRS amplitude variations in abdominal ECG recordings

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Power-MF: robust fetal QRS detection from non-invasive fetal electrocardiogram recordings;Physiological Measurement;2024-05-01

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