A deep learning approach for fetal QRS complex detection-Reference-Cited by-同舟云学术

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A deep learning approach for fetal QRS complex detection

Published:2018-04-20 Issue:4 Volume:39 Page:045004
ISSN:1361-6579
Container-title:Physiological Measurement
language:
Short-container-title:Physiol. Meas.

Author:

Zhong Wei^ORCID,Liao Lijuan,Guo Xuemei,Wang Guoli^ORCID

Funder

National Natural Science Foundation of China

Key Program of Natural Science Foundation of Guangdong, China

Publisher

IOP Publishing

Subject

Physiology (medical),Biomedical Engineering,Physiology,Biophysics

Link

http://iopscience.iop.org/article/10.1088/1361-6579/aab297/pdf

Reference26 articles.

1. An open-source framework for stress-testing non-invasive foetal ECG extraction algorithms

2. Guidelines and recommendations for safe use of Doppler ultrasound in perinatal applications

3. A practical guide to non-invasive foetal electrocardiogram extraction and analysis

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1. A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network;2024-04-19

2. Classification of Fetal Cardiac Arrhythmia Using Heart Rate Variability and Machine Learning;2024 5th International Conference on Advancements in Computational Sciences (ICACS);2024-02-19

3. An Effective Integrated Framework for Fetal QRS Complex Detection Based on Abdominal ECG Signal;Journal of Medical and Biological Engineering;2024-02

4. An Integrated Framework for Assessing the Quality of Non-invasive Fetal Electrocardiography Signals;Journal of Medical and Biological Engineering;2024-02

5. Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring;Archives of Computational Methods in Engineering;2024-01-31

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