{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T06:31:54Z","timestamp":1781505114557,"version":"3.54.1"},"reference-count":31,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,29]],"date-time":"2021-07-29T00:00:00Z","timestamp":1627516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["HI18C2383"],"award-info":[{"award-number":["HI18C2383"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["HI18C0022"],"award-info":[{"award-number":["HI18C0022"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2019R1A2C4069504"],"award-info":[{"award-number":["2019R1A2C4069504"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s21155130","type":"journal-article","created":{"date-parts":[[2021,7,29]],"date-time":"2021-07-29T21:21:21Z","timestamp":1627593681000},"page":"5130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Estimation of Stroke Volume Variance from Arterial Blood Pressure: Using a 1-D Convolutional Neural Network"],"prefix":"10.3390","volume":"21","author":[{"given":"Hye-Mee","family":"Kwon","sequence":"first","affiliation":[{"name":"Asan Medical Center, Department of Anesthesiology and Pain Medicine, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Seoul 05505, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5444-3320","authenticated-orcid":false,"given":"Woo-Young","family":"Seo","sequence":"additional","affiliation":[{"name":"Biomedical Engneering Research Center, Asan Medical Center, Seoul 05505, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae-Man","family":"Kim","sequence":"additional","affiliation":[{"name":"Biomedical Engneering Research Center, Asan Medical Center, Seoul 05505, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Woo-Hyun","family":"Shim","sequence":"additional","affiliation":[{"name":"Asan Medical Center, Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul 05505, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung-Hoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Asan Medical Center, Department of Anesthesiology and Pain Medicine, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Seoul 05505, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3627-1107","authenticated-orcid":false,"given":"Gyu-Sam","family":"Hwang","sequence":"additional","affiliation":[{"name":"Asan Medical Center, Department of Anesthesiology and Pain Medicine, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Seoul 05505, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1097\/00000542-196709000-00021","article-title":"Hemodynamic effects of changes in arterial carbon dioxide tension during intermittent positive pressure ventilation","volume":"28","author":"Morgan","year":"1967","journal-title":"Anesthesiology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1164\/rccm.201801-0088CI","article-title":"Arterial Pulse Pressure Variation with Mechanical Ventilation","volume":"199","author":"Teboul","year":"2019","journal-title":"Am. 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