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However, current blood pressure measurement methods have intrinsic limitations; for instance, arterial blood pressure is measured by inserting a catheter in the artery causing discomfort and infection.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Method<\/jats:title>\n                <jats:p>Photoplethysmogram (PPG) signals can be collected via non-invasive devices, and therefore have stimulated researchers\u2019 interest in exploring blood pressure estimation using machine learning and PPG signals as a non-invasive alternative. In this paper, we propose a Transformer-based deep learning architecture that utilizes PPG signals to conduct a personalized estimation of arterial systolic blood pressure, arterial diastolic blood pressure, and oxygen saturation.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The proposed method was evaluated with a subset of 1,732 subjects from the publicly available ICU dataset MIMIC III. The mean absolute error is 2.52\u2009\u00b1\u20092.43\u00a0mmHg for systolic blood pressure, 1.37\u2009\u00b1\u20091.89\u00a0mmHg for diastolic blood pressure, and 0.58\u2009\u00b1\u20090.79% for oxygen saturation, which satisfies the requirements of the Association of Advancement of Medical Instrumentation standard and achieve grades A for the British Hypertension Society standard.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The results indicate that our model meets clinical standards and could potentially boost the accuracy of blood pressure and oxygen saturation measurement to deliver high-quality healthcare.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-023-02215-2","type":"journal-article","created":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T12:02:01Z","timestamp":1689940921000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Non-invasive arterial blood pressure measurement and SpO2 estimation using PPG signal: a deep learning framework"],"prefix":"10.1186","volume":"23","author":[{"given":"Yan","family":"Chu","sequence":"first","affiliation":[]},{"given":"Kaichen","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Yu-Chun","family":"Hsu","sequence":"additional","affiliation":[]},{"given":"Tongtong","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Dulin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Wentao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Sean I.","family":"Savitz","sequence":"additional","affiliation":[]},{"given":"Xiaoqian","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Shayan","family":"Shams","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,21]]},"reference":[{"issue":"9859","key":"2215_CR1","doi-asserted-by":"publisher","first-page":"2095","DOI":"10.1016\/S0140-6736(12)61728-0","volume":"380","author":"R Lozano","year":"2012","unstructured":"Lozano R, et al. 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