Generation of Seismocardiography Heartbeats Using a Wasserstein Generative Adversarial Network With Feature Control
<italic>Goal:</italic> Seismocardiography (SCG) offers critical insights into cardiac performance, but its analysis often faces challenges due to the limited availability of data. This study aims to generate synthetic SCG heartbeats which can augment existing datasets to enable more rese...
Saved in:
Main Authors: | James Skoric, Yannick D'Mello, David V. Plant |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Open Journal of Engineering in Medicine and Biology |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10731564/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
MSCardio seismocardiography dataset: Initial insights from remote monitoring of cardiovascular-induced chest vibrations via smartphonesZenodo
by: Amirtahà Taebi, et al.
Published: (2025-08-01) -
Physically Informed Synthetic Data Generation and U-Net Generative Adversarial Network for Palimpsest Reconstruction
by: Jose L. Salmeron, et al.
Published: (2025-07-01) -
Automatic text generation system for endangered languages based on conditional generative adversarial networks
by: Zhong Luo
Published: (2025-12-01) -
Application study on conditional generative adversarial network (cGAN) to generate ballast particles for discrete element method simulation
by: Viet Dinh Le, et al.
Published: (2025-12-01) -
Assessing the Fidelity and Utility of Water Systems Data Using Generative Adversarial Networks: A Technical Review
by: Md Nazmul Kabir Sikder, et al.
Published: (2025-01-01)