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Modeling the Pulse Signal by Wave-Shape Function and Analyzing by Synchrosqueezing Transform

We apply the recently developed adaptive non-harmonic model based on the wave-shape function, as well as the time-frequency analysis tool called synchrosqueezing transform (SST) to model and analyze oscillatory physiological signals. To demonstrate how the model and algorithm work, we apply them to...

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Detalles Bibliográficos
Autores principales: Wu, Hau-Tieng, Wu, Han-Kuei, Wang, Chun-Li, Yang, Yueh-Lung, Wu, Wen-Hsiang, Tsai, Tung-Hu, Chang, Hen-Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4909275/
https://www.ncbi.nlm.nih.gov/pubmed/27304979
http://dx.doi.org/10.1371/journal.pone.0157135
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author Wu, Hau-Tieng
Wu, Han-Kuei
Wang, Chun-Li
Yang, Yueh-Lung
Wu, Wen-Hsiang
Tsai, Tung-Hu
Chang, Hen-Hong
author_facet Wu, Hau-Tieng
Wu, Han-Kuei
Wang, Chun-Li
Yang, Yueh-Lung
Wu, Wen-Hsiang
Tsai, Tung-Hu
Chang, Hen-Hong
author_sort Wu, Hau-Tieng
collection PubMed
description We apply the recently developed adaptive non-harmonic model based on the wave-shape function, as well as the time-frequency analysis tool called synchrosqueezing transform (SST) to model and analyze oscillatory physiological signals. To demonstrate how the model and algorithm work, we apply them to study the pulse wave signal. By extracting features called the spectral pulse signature, and based on functional regression, we characterize the hemodynamics from the radial pulse wave signals recorded by the sphygmomanometer. Analysis results suggest the potential of the proposed signal processing approach to extract health-related hemodynamics features.
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spelling pubmed-49092752016-07-06 Modeling the Pulse Signal by Wave-Shape Function and Analyzing by Synchrosqueezing Transform Wu, Hau-Tieng Wu, Han-Kuei Wang, Chun-Li Yang, Yueh-Lung Wu, Wen-Hsiang Tsai, Tung-Hu Chang, Hen-Hong PLoS One Research Article We apply the recently developed adaptive non-harmonic model based on the wave-shape function, as well as the time-frequency analysis tool called synchrosqueezing transform (SST) to model and analyze oscillatory physiological signals. To demonstrate how the model and algorithm work, we apply them to study the pulse wave signal. By extracting features called the spectral pulse signature, and based on functional regression, we characterize the hemodynamics from the radial pulse wave signals recorded by the sphygmomanometer. Analysis results suggest the potential of the proposed signal processing approach to extract health-related hemodynamics features. Public Library of Science 2016-06-15 /pmc/articles/PMC4909275/ /pubmed/27304979 http://dx.doi.org/10.1371/journal.pone.0157135 Text en © 2016 Wu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wu, Hau-Tieng
Wu, Han-Kuei
Wang, Chun-Li
Yang, Yueh-Lung
Wu, Wen-Hsiang
Tsai, Tung-Hu
Chang, Hen-Hong
Modeling the Pulse Signal by Wave-Shape Function and Analyzing by Synchrosqueezing Transform
title Modeling the Pulse Signal by Wave-Shape Function and Analyzing by Synchrosqueezing Transform
title_full Modeling the Pulse Signal by Wave-Shape Function and Analyzing by Synchrosqueezing Transform
title_fullStr Modeling the Pulse Signal by Wave-Shape Function and Analyzing by Synchrosqueezing Transform
title_full_unstemmed Modeling the Pulse Signal by Wave-Shape Function and Analyzing by Synchrosqueezing Transform
title_short Modeling the Pulse Signal by Wave-Shape Function and Analyzing by Synchrosqueezing Transform
title_sort modeling the pulse signal by wave-shape function and analyzing by synchrosqueezing transform
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4909275/
https://www.ncbi.nlm.nih.gov/pubmed/27304979
http://dx.doi.org/10.1371/journal.pone.0157135
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