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Estimation of Stroke Volume Variance from Arterial Blood Pressure: Using a 1-D Convolutional Neural Network

Background: We aimed to create a novel model using a deep learning method to estimate stroke volume variation (SVV), a widely used predictor of fluid responsiveness, from arterial blood pressure waveform (ABPW). Methods: In total, 557 patients and 8,512,564 SVV datasets were collected and were divid...

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Autores principales: Kwon, Hye-Mee, Seo, Woo-Young, Kim, Jae-Man, Shim, Woo-Hyun, Kim, Sung-Hoon, Hwang, Gyu-Sam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347322/
https://www.ncbi.nlm.nih.gov/pubmed/34372366
http://dx.doi.org/10.3390/s21155130
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author Kwon, Hye-Mee
Seo, Woo-Young
Kim, Jae-Man
Shim, Woo-Hyun
Kim, Sung-Hoon
Hwang, Gyu-Sam
author_facet Kwon, Hye-Mee
Seo, Woo-Young
Kim, Jae-Man
Shim, Woo-Hyun
Kim, Sung-Hoon
Hwang, Gyu-Sam
author_sort Kwon, Hye-Mee
collection PubMed
description Background: We aimed to create a novel model using a deep learning method to estimate stroke volume variation (SVV), a widely used predictor of fluid responsiveness, from arterial blood pressure waveform (ABPW). Methods: In total, 557 patients and 8,512,564 SVV datasets were collected and were divided into three groups: training, validation, and test. Data was composed of 10 s of ABPW and corresponding SVV data recorded every 2 s. We built a convolutional neural network (CNN) model to estimate SVV from the ABPW with pre-existing commercialized model (EV1000) as a reference. We applied pre-processing, multichannel, and dimension reduction to improve the CNN model with diversified inputs. Results: Our CNN model showed an acceptable performance with sample data (r = 0.91, MSE = 6.92). Diversification of inputs, such as normalization, frequency, and slope of ABPW significantly improved the model correlation (r = 0.95), lowered mean squared error (MSE = 2.13), and resulted in a high concordance rate (96.26%) with the SVV from the commercialized model. Conclusions: We developed a new CNN deep-learning model to estimate SVV. Our CNN model seems to be a viable alternative when the necessary medical device is not available, thereby allowing a wider range of application and resulting in optimal patient management.
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spelling pubmed-83473222021-08-08 Estimation of Stroke Volume Variance from Arterial Blood Pressure: Using a 1-D Convolutional Neural Network Kwon, Hye-Mee Seo, Woo-Young Kim, Jae-Man Shim, Woo-Hyun Kim, Sung-Hoon Hwang, Gyu-Sam Sensors (Basel) Article Background: We aimed to create a novel model using a deep learning method to estimate stroke volume variation (SVV), a widely used predictor of fluid responsiveness, from arterial blood pressure waveform (ABPW). Methods: In total, 557 patients and 8,512,564 SVV datasets were collected and were divided into three groups: training, validation, and test. Data was composed of 10 s of ABPW and corresponding SVV data recorded every 2 s. We built a convolutional neural network (CNN) model to estimate SVV from the ABPW with pre-existing commercialized model (EV1000) as a reference. We applied pre-processing, multichannel, and dimension reduction to improve the CNN model with diversified inputs. Results: Our CNN model showed an acceptable performance with sample data (r = 0.91, MSE = 6.92). Diversification of inputs, such as normalization, frequency, and slope of ABPW significantly improved the model correlation (r = 0.95), lowered mean squared error (MSE = 2.13), and resulted in a high concordance rate (96.26%) with the SVV from the commercialized model. Conclusions: We developed a new CNN deep-learning model to estimate SVV. Our CNN model seems to be a viable alternative when the necessary medical device is not available, thereby allowing a wider range of application and resulting in optimal patient management. MDPI 2021-07-29 /pmc/articles/PMC8347322/ /pubmed/34372366 http://dx.doi.org/10.3390/s21155130 Text en © 2021 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
Kwon, Hye-Mee
Seo, Woo-Young
Kim, Jae-Man
Shim, Woo-Hyun
Kim, Sung-Hoon
Hwang, Gyu-Sam
Estimation of Stroke Volume Variance from Arterial Blood Pressure: Using a 1-D Convolutional Neural Network
title Estimation of Stroke Volume Variance from Arterial Blood Pressure: Using a 1-D Convolutional Neural Network
title_full Estimation of Stroke Volume Variance from Arterial Blood Pressure: Using a 1-D Convolutional Neural Network
title_fullStr Estimation of Stroke Volume Variance from Arterial Blood Pressure: Using a 1-D Convolutional Neural Network
title_full_unstemmed Estimation of Stroke Volume Variance from Arterial Blood Pressure: Using a 1-D Convolutional Neural Network
title_short Estimation of Stroke Volume Variance from Arterial Blood Pressure: Using a 1-D Convolutional Neural Network
title_sort estimation of stroke volume variance from arterial blood pressure: using a 1-d convolutional neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347322/
https://www.ncbi.nlm.nih.gov/pubmed/34372366
http://dx.doi.org/10.3390/s21155130
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