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Non-invasive arterial blood pressure measurement and SpO~2~ estimation using PPG signal: a deep learning framework by Chu, Yan (author);Tang, Kaichen (author);Hsu, Yu-Chun (author);Huang, Tongtong (author);Wang, Dulin (author);Li, Wentao (author);Savitz, Sean I. (author);Jiang, Xiaoqian (author);Shams, Shayan (author) is a Health Professions article available to read on EtoBox.

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## Background Monitoring blood pressure and peripheral capillary oxygen saturation plays a crucial role in healthcare management for patients with chronic diseases, especially hypertension and vascular disease. 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. ## Method Photoplethysmogram (PPG) signals can be collected via non-invasive devices, and therefore have stimulated researchers’ 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. ## Results 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 ± 2.43 mmHg for systolic blood pressure, 1.37 ± 1.89 mmHg for diastolic blood pressure, and 0.58 ± 0.79% for oxygen saturation, which satisfies the r

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Author

Chu, Yan (author);Tang, Kaichen (author);Hsu, Yu-Chun (author);Huang, Tongtong (author);Wang, Dulin (author);Li, Wentao (author);Savitz, Sean I. (author);Jiang, Xiaoqian (author);Shams, Shayan (author)

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Health Professions (Health Sciences)