Deep Learning for Sparse Domain Kalman Filtering With Applications on ECG Denoising and Motility Estimation-Reference-Cited by-同舟云学术

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Deep Learning for Sparse Domain Kalman Filtering With Applications on ECG Denoising and Motility Estimation

Published:2024-08 Issue:8 Volume:71 Page:2321-2329
ISSN:0018-9294
Container-title:IEEE Transactions on Biomedical Engineering
language:
Short-container-title:IEEE Trans. Biomed. Eng.

Author:

de Vries I.R.¹^ORCID,de Jong A.M.²^ORCID,van der Hout-van der Jagt M.B.³^ORCID,van Laar J.O.E.H.³^ORCID,Vullings R.⁴^ORCID

Affiliation:

1. Department of Obstetrics and Gynecology of the Máxima Medical Centre, Eindhoven, Noord-Brabant, The Netherlands

2. Department of Applied Physics and Science Education, Eindhoven University of Technology, The Netherlands

3. Department of Obstetrics and Gynecology of the Máxima Medical Centre, The Netherlands

4. Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands

Funder

ZonMW

Topspecialistische Zorg en Onderzoek

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/10/10604459/10443058.pdf?arnumber=10443058

Reference36 articles.

1. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

2. A Survey of Sparse Representation: Algorithms and Applications

3. Is the normal heart a periodic oscillator?

4. ECG Denoising and Compression Using a Modified Extended Kalman Filter Structure

5. The origin and characterization of the primary signal, noise, and interference sources in the high frequency electrocardiogram

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1. Unsupervised denoising of the non-invasive fetal electrocardiogram with sparse domain Kalman filtering and vectorcardiographic loop alignment;Physiological Measurement;2024-07-01

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