Modeling the pulse signal by wave-shape function and analyzing by synchrosqueezing transform

Hau Tieng Wu; Han Kuei Wu; Chun Li Wang; Yueh Lung Yang; Wen Hsiang Wu; Tung Hu Tsai; Hen Hong Chang

doi:10.1371/journal.pone.0157135

Modeling the pulse signal by wave-shape function and analyzing by synchrosqueezing transform

Hau Tieng Wu, Han Kuei Wu, Chun Li Wang, Yueh Lung Yang, Wen Hsiang Wu, Tung Hu Tsai, Hen Hong Chang

Research output: Contribution to journal › Journal Article › peer-review

19 Scopus citations

Abstract

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.

Original language	English
Article number	e0157135
Journal	PLoS ONE
Volume	11
Issue number	6
DOIs	https://doi.org/10.1371/journal.pone.0157135
State	Published - 01 06 2016
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2016 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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abstract = "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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AU - Wu, Hau Tieng

AU - Wu, Han Kuei

AU - Wang, Chun Li

AU - Yang, Yueh Lung

AU - Wu, Wen Hsiang

AU - Tsai, Tung Hu

AU - Chang, Hen Hong

N1 - Publisher Copyright: © 2016 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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N2 - 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.

AB - 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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Modeling the pulse signal by wave-shape function and analyzing by synchrosqueezing transform

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