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

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Abstract
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.
Author(s)
권혜미서우영김재만심우현김성훈황규삼
Issued Date
2021
Type
Article
Keyword
CNN modeldeep learningdiagnosticmechanical ventilationpredictionstroke volume variance
DOI
10.3390/s21155130
URI
https://oak.ulsan.ac.kr/handle/2021.oak/6970
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_3dd9c9812fdb4c89a07d233d7010dc51&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Estimation%20of%20Stroke%20Volume%20Variance%20from%20Arterial%20Blood%20Pressure:%20Using%20a%201-D%20Convolutional%20Neural%20Network&offset=0&pcAvailability=true
Publisher
SENSORS
Location
스위스
Language
영어
ISSN
1424-8220
Citation Volume
21
Citation Number
15
Citation Start Page
0
Citation End Page
0
Appears in Collections:
Medicine > Medicine
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