A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography

A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography

Non-invasive foetal electrocardiography (NI-FECG) has become an important prenatal monitoring method in the hospital. However, due to its susceptibility to non-stationary noise sources and lack of robust extraction methods, the capture of high-quality NI-FECG remains a challenge. Recording waveforms...

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Bibliographic Details
Main Authors: Clifton, D.A (Author), Li, Y. (Author), Liu, Z. (Author), Long, Y. (Author), Mertes, G. (Author), Yang, Y. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Biomedical signal processing
Convolution
convolutional neural network
Convolutional neural network
Convolutional neural networks
Deep learning
Electrocardiography
Fetal ECG
Fetal electrocardiographies
foetal ECG
High quality
Hospitals
Learning approach
Monitoring methods
Noise source
Nonstationary noise
signal quality
Signal quality
Waveforms
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