{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:04:00Z","timestamp":1755219840872,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>Continuous arterial blood pressure (ABP) monitoring is essential for real-time hemodynamic assessment but is limited by the invasiveness of arterial catheterization. We propose a ResUNet-based deep learning framework that generates high-fidelity ABP waveforms from photoplethysmogram (PPG) signals, incorporating periodic calibration values applied every 2.5 minutes. Trained on intraoperative data from 4,687 surgical cases at Seoul National University Hospital, the calibrated model achieved a mean absolute error (MAE) of 5.69 mmHg with a standard deviation (SD) of 3.76 mmHg, outperforming the non-calibrated model (MAE: 9.47 mmHg [SD: 5.52]). It satisfied both AAMI and BHS standards, achieving Grade B for SBP and Grade A for DBP. This method enables accurate non-invasive ABP estimation and supports clinical deployment in settings where invasive monitoring is infeasible.<\/jats:p>","DOI":"10.3233\/shti251277","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:46:56Z","timestamp":1754567216000},"source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Based Arterial Blood Pressure Waveform Generation Using Photoplethysmography for Non-Invasive Hemodynamic Monitoring"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2942-253X","authenticated-orcid":false,"given":"Sangha","family":"Kim","sequence":"first","affiliation":[{"name":"Healthcare AI Research Institute, Seoul National University Hospital, Seoul, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"IMPACT consortium","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyeonhoon","family":"Lee","sequence":"additional","affiliation":[{"name":"Healthcare AI Research Institute, Seoul National University Hospital, Seoul, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seong-A","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyun-Lim","family":"Yang","sequence":"additional","affiliation":[{"name":"Office of Hospital Information, Seoul National University Hospital, Seoul, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ho Geol","family":"Ryu","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyung-Chul","family":"Lee","sequence":"additional","affiliation":[{"name":"Healthcare AI Research Institute, Seoul National University Hospital, Seoul, Republic of Korea"},{"name":"Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251277","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:46:57Z","timestamp":1754567217000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251277"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251277","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}