Improved Electrode Motion Artefact Denoising in ECG Using Convolutional Neural Networks and a Custom Loss Function-Reference-Cited by-同舟云学术

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Improved Electrode Motion Artefact Denoising in ECG Using Convolutional Neural Networks and a Custom Loss Function

Published:2022 Issue: Volume:10 Page:54891-54898
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Brophy Eoin¹^ORCID,Hennelly Bryan²,De Vos Maarten³,Boylan Geraldine⁴,Ward Tomas¹^ORCID

Affiliation:

1. School of Computing, Dublin City University, Dublin 9, Ireland

2. Department of Electronic Engineering, Maynooth University, Maynooth, Ireland

3. Department of Electrical Engineering, KU Leuven, Leuven, Belgium

4. INFANT Research Centre, University College Cork, Cork, Ireland

Funder

Science Foundation Ireland

Flemish Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09779754.pdf?arnumber=9779754

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1. Automated Optical Inspection for Quality Control in PCBA assembly lines: a case study for Point of Sale Devices Production Lines;2024 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA);2024-05-23

2. Anti-Motion Interference Electrocardiograph Monitoring System: A Review;IEEE Sensors Journal;2024-05-15

3. Exploring Convolutional Neural Network Architectures for EEG Feature Extraction;Sensors;2024-01-29

4. Multiscale Convolution and Attention based Denoising Autoencoder for Motion Artifact Removal in ECG Signals;Proceedings of the 2024 7th International Conference on Image and Graphics Processing;2024-01-19

5. Intelligent Support for Cardiovascular Diagnosis;Advances in Media, Entertainment, and the Arts;2024-01-10

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