Publication Date
12-1-2023
Document Type
Article
Publication Title
BMC Medical Informatics and Decision Making
Volume
23
Issue
1
DOI
10.1186/s12911-023-02215-2
Abstract
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 requirements of the Association of Advancement of Medical Instrumentation standard and achieve grades A for the British Hypertension Society standard. Conclusions: 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.
Funding Number
180012
Funding Sponsor
National Institutes of Health
Keywords
Blood pressure, Deep learning, Digital health, Hypertension assessment, Oxygen saturation, Photoplethysmogram
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Applied Data Science
Recommended Citation
Yan Chu, Kaichen Tang, Yu Chun Hsu, Tongtong Huang, Dulin Wang, Wentao Li, Sean I. Savitz, Xiaoqian Jiang, and Shayan Shams. "Non-invasive arterial blood pressure measurement and SpO2 estimation using PPG signal: a deep learning framework" BMC Medical Informatics and Decision Making (2023). https://doi.org/10.1186/s12911-023-02215-2
