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Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study

BACKGROUND: For the noninvasive assessment of arterial stiffness, a well-known indicator of arterial aging, various features based on the photoplethysmogram and regression methods have been proposed. However, whether because of the existing characteristics not accurately reflecting the characteristi...

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Autores principales: Park, Junyung, Shin, Hangsik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8972117/
https://www.ncbi.nlm.nih.gov/pubmed/35297776
http://dx.doi.org/10.2196/33439
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author Park, Junyung
Shin, Hangsik
author_facet Park, Junyung
Shin, Hangsik
author_sort Park, Junyung
collection PubMed
description BACKGROUND: For the noninvasive assessment of arterial stiffness, a well-known indicator of arterial aging, various features based on the photoplethysmogram and regression methods have been proposed. However, whether because of the existing characteristics not accurately reflecting the characteristics of the incident and reflected waveforms of the photoplethysmogram or because of the lack of expressive power of the regression model, a reliable arterial stiffness assessment technique based on a single photoplethysmogram has not yet been proposed. OBJECTIVE: The purpose of this study is to discover highly correlated features from the incident and reflected waves decomposed from a photoplethysmogram waveform and to develop an artificial neural network-based regression model for the assessment of vascular aging using newly derived features. METHODS: We obtained photoplethysmograms from 757 participants. All recorded photoplethysmograms were segmented for each beat, and each waveform was decomposed into incident and reflected waves by the Gaussian mixture model. The 26 basic features and 52 combined features were defined from the morphological characteristics of the incident and reflected waves. The regression model of the artificial neural network was developed using the defined features. RESULTS: In correlation analysis, the features from the amplitude of the reflected wave and the skewness of the photoplethysmogram showed a relatively strong correlation with the participant’s real age. In the estimation of real age, the artificial neural network model showed 10.0 years of root mean square error. Its estimated age and real age had a strong correlation of 0.63 (P<.001). CONCLUSIONS: This study proved that the features defined from the reflected wave and skewness of the photoplethysmogram are useful to assess vascular aging. Moreover, the regression model of artificial neural network using these features shows the feasibility for the estimation of vascular aging.
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spelling pubmed-89721172022-04-02 Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study Park, Junyung Shin, Hangsik JMIR Med Inform Original Paper BACKGROUND: For the noninvasive assessment of arterial stiffness, a well-known indicator of arterial aging, various features based on the photoplethysmogram and regression methods have been proposed. However, whether because of the existing characteristics not accurately reflecting the characteristics of the incident and reflected waveforms of the photoplethysmogram or because of the lack of expressive power of the regression model, a reliable arterial stiffness assessment technique based on a single photoplethysmogram has not yet been proposed. OBJECTIVE: The purpose of this study is to discover highly correlated features from the incident and reflected waves decomposed from a photoplethysmogram waveform and to develop an artificial neural network-based regression model for the assessment of vascular aging using newly derived features. METHODS: We obtained photoplethysmograms from 757 participants. All recorded photoplethysmograms were segmented for each beat, and each waveform was decomposed into incident and reflected waves by the Gaussian mixture model. The 26 basic features and 52 combined features were defined from the morphological characteristics of the incident and reflected waves. The regression model of the artificial neural network was developed using the defined features. RESULTS: In correlation analysis, the features from the amplitude of the reflected wave and the skewness of the photoplethysmogram showed a relatively strong correlation with the participant’s real age. In the estimation of real age, the artificial neural network model showed 10.0 years of root mean square error. Its estimated age and real age had a strong correlation of 0.63 (P<.001). CONCLUSIONS: This study proved that the features defined from the reflected wave and skewness of the photoplethysmogram are useful to assess vascular aging. Moreover, the regression model of artificial neural network using these features shows the feasibility for the estimation of vascular aging. JMIR Publications 2022-03-17 /pmc/articles/PMC8972117/ /pubmed/35297776 http://dx.doi.org/10.2196/33439 Text en ©Junyung Park, Hangsik Shin. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 17.03.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Park, Junyung
Shin, Hangsik
Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study
title Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study
title_full Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study
title_fullStr Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study
title_full_unstemmed Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study
title_short Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study
title_sort vascular aging estimation based on artificial neural network using photoplethysmogram waveform decomposition: retrospective cohort study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8972117/
https://www.ncbi.nlm.nih.gov/pubmed/35297776
http://dx.doi.org/10.2196/33439
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