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Towards Generating Realistic Wrist Pulse Signals Using Enhanced One Dimensional Wasserstein GAN

For the past several years, there has been an increasing focus on deep learning methods applied into computational pulse diagnosis. However, one factor restraining its development lies in the small wrist pulse dataset, due to privacy risks or lengthy experiments cost. In this study, for the first ti...

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Autores principales: Chang, Jiaxing, Hu, Fei, Xu, Huaxing, Mao, Xiaobo, Zhao, Yuping, Huang, Luqi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921956/
https://www.ncbi.nlm.nih.gov/pubmed/36772488
http://dx.doi.org/10.3390/s23031450
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author Chang, Jiaxing
Hu, Fei
Xu, Huaxing
Mao, Xiaobo
Zhao, Yuping
Huang, Luqi
author_facet Chang, Jiaxing
Hu, Fei
Xu, Huaxing
Mao, Xiaobo
Zhao, Yuping
Huang, Luqi
author_sort Chang, Jiaxing
collection PubMed
description For the past several years, there has been an increasing focus on deep learning methods applied into computational pulse diagnosis. However, one factor restraining its development lies in the small wrist pulse dataset, due to privacy risks or lengthy experiments cost. In this study, for the first time, we address the challenging by presenting a novel one-dimension generative adversarial networks (GAN) for generating wrist pulse signals, which manages to learn a mapping strategy from a random noise space to the original wrist pulse data distribution automatically. Concretely, Wasserstein GAN with gradient penalty (WGAN-GP) is employed to alleviate the mode collapse problem of vanilla GANs, which could be able to further enhance the performance of the generated pulse data. We compared our proposed model performance with several typical GAN models, including vanilla GAN, deep convolutional GAN (DCGAN) and Wasserstein GAN (WGAN). To verify the feasibility of the proposed algorithm, we trained our model with a dataset of real recorded wrist pulse signals. In conducted experiments, qualitative visual inspection and several quantitative metrics, such as maximum mean deviation (MMD), sliced Wasserstein distance (SWD) and percent root mean square difference (PRD), are examined to measure performance comprehensively. Overall, WGAN-GP achieves the best performance and quantitative results show that the above three metrics can be as low as 0.2325, 0.0112 and 5.8748, respectively. The positive results support that generating wrist pulse data from a small ground truth is possible. Consequently, our proposed WGAN-GP model offers a potential innovative solution to address data scarcity challenge for researchers working with computational pulse diagnosis, which are expected to improve the performance of pulse diagnosis algorithms in the future.
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spelling pubmed-99219562023-02-12 Towards Generating Realistic Wrist Pulse Signals Using Enhanced One Dimensional Wasserstein GAN Chang, Jiaxing Hu, Fei Xu, Huaxing Mao, Xiaobo Zhao, Yuping Huang, Luqi Sensors (Basel) Article For the past several years, there has been an increasing focus on deep learning methods applied into computational pulse diagnosis. However, one factor restraining its development lies in the small wrist pulse dataset, due to privacy risks or lengthy experiments cost. In this study, for the first time, we address the challenging by presenting a novel one-dimension generative adversarial networks (GAN) for generating wrist pulse signals, which manages to learn a mapping strategy from a random noise space to the original wrist pulse data distribution automatically. Concretely, Wasserstein GAN with gradient penalty (WGAN-GP) is employed to alleviate the mode collapse problem of vanilla GANs, which could be able to further enhance the performance of the generated pulse data. We compared our proposed model performance with several typical GAN models, including vanilla GAN, deep convolutional GAN (DCGAN) and Wasserstein GAN (WGAN). To verify the feasibility of the proposed algorithm, we trained our model with a dataset of real recorded wrist pulse signals. In conducted experiments, qualitative visual inspection and several quantitative metrics, such as maximum mean deviation (MMD), sliced Wasserstein distance (SWD) and percent root mean square difference (PRD), are examined to measure performance comprehensively. Overall, WGAN-GP achieves the best performance and quantitative results show that the above three metrics can be as low as 0.2325, 0.0112 and 5.8748, respectively. The positive results support that generating wrist pulse data from a small ground truth is possible. Consequently, our proposed WGAN-GP model offers a potential innovative solution to address data scarcity challenge for researchers working with computational pulse diagnosis, which are expected to improve the performance of pulse diagnosis algorithms in the future. MDPI 2023-01-28 /pmc/articles/PMC9921956/ /pubmed/36772488 http://dx.doi.org/10.3390/s23031450 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chang, Jiaxing
Hu, Fei
Xu, Huaxing
Mao, Xiaobo
Zhao, Yuping
Huang, Luqi
Towards Generating Realistic Wrist Pulse Signals Using Enhanced One Dimensional Wasserstein GAN
title Towards Generating Realistic Wrist Pulse Signals Using Enhanced One Dimensional Wasserstein GAN
title_full Towards Generating Realistic Wrist Pulse Signals Using Enhanced One Dimensional Wasserstein GAN
title_fullStr Towards Generating Realistic Wrist Pulse Signals Using Enhanced One Dimensional Wasserstein GAN
title_full_unstemmed Towards Generating Realistic Wrist Pulse Signals Using Enhanced One Dimensional Wasserstein GAN
title_short Towards Generating Realistic Wrist Pulse Signals Using Enhanced One Dimensional Wasserstein GAN
title_sort towards generating realistic wrist pulse signals using enhanced one dimensional wasserstein gan
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921956/
https://www.ncbi.nlm.nih.gov/pubmed/36772488
http://dx.doi.org/10.3390/s23031450
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