Deep-learning-based blood pressure estimation using multi channel photoplethysmogram and finger pressure with attention mechanism
Abstract Recently, several studies have proposed methods for measuring cuffless blood pressure (BP) using finger photoplethysmogram (PPG) signals. This study presents a new BP estimation system that measures PPG signals under progressive finger pressure, making the system relatively robust to errors...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-36068-6 |
| _version_ | 1797806765863075840 |
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| author | Jehyun Kyung Joon-Young Yang Jeong-Hwan Choi Joon-Hyuk Chang Sangkon Bae Jinwoo Choi Younho Kim |
| author_facet | Jehyun Kyung Joon-Young Yang Jeong-Hwan Choi Joon-Hyuk Chang Sangkon Bae Jinwoo Choi Younho Kim |
| author_sort | Jehyun Kyung |
| collection | DOAJ |
| description | Abstract Recently, several studies have proposed methods for measuring cuffless blood pressure (BP) using finger photoplethysmogram (PPG) signals. This study presents a new BP estimation system that measures PPG signals under progressive finger pressure, making the system relatively robust to errors caused by finger position when using the cuffless oscillometric method. To reduce errors caused by finger position, we developed a sensor that can simultaneously measure multi-channel PPG and force signals in a wide field of view (FOV). We propose a deep-learning-based algorithm that can learn to focus on the optimal PPG channel from multi channel PPG using an attention mechanism. The errors (ME ± STD) of the proposed multi channel system were 0.43±9.35 mmHg and 0.21 ± 7.72 mmHg for SBP and DBP, respectively. Through extensive experiments, we found a significant performance difference depending on the location of the PPG measurement in the BP estimation system using finger pressure. |
| first_indexed | 2024-03-13T06:12:19Z |
| format | Article |
| id | doaj.art-35752d82d8bb4901a2a730046fac54ea |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T06:12:19Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-35752d82d8bb4901a2a730046fac54ea2023-06-11T11:10:15ZengNature PortfolioScientific Reports2045-23222023-06-0113111210.1038/s41598-023-36068-6Deep-learning-based blood pressure estimation using multi channel photoplethysmogram and finger pressure with attention mechanismJehyun Kyung0Joon-Young Yang1Jeong-Hwan Choi2Joon-Hyuk Chang3Sangkon Bae4Jinwoo Choi5Younho Kim6Department of Electronic Engineering, Hanyang UniversityDepartment of Electronic Engineering, Hanyang UniversityDepartment of Electronic Engineering, Hanyang UniversityDepartment of Electronic Engineering, Hanyang UniversitySAIT, Samsung Electronics, Advanced Sensor LabSAIT, Samsung Electronics, Advanced Sensor LabSAIT, Samsung Electronics, Advanced Sensor LabAbstract Recently, several studies have proposed methods for measuring cuffless blood pressure (BP) using finger photoplethysmogram (PPG) signals. This study presents a new BP estimation system that measures PPG signals under progressive finger pressure, making the system relatively robust to errors caused by finger position when using the cuffless oscillometric method. To reduce errors caused by finger position, we developed a sensor that can simultaneously measure multi-channel PPG and force signals in a wide field of view (FOV). We propose a deep-learning-based algorithm that can learn to focus on the optimal PPG channel from multi channel PPG using an attention mechanism. The errors (ME ± STD) of the proposed multi channel system were 0.43±9.35 mmHg and 0.21 ± 7.72 mmHg for SBP and DBP, respectively. Through extensive experiments, we found a significant performance difference depending on the location of the PPG measurement in the BP estimation system using finger pressure.https://doi.org/10.1038/s41598-023-36068-6 |
| spellingShingle | Jehyun Kyung Joon-Young Yang Jeong-Hwan Choi Joon-Hyuk Chang Sangkon Bae Jinwoo Choi Younho Kim Deep-learning-based blood pressure estimation using multi channel photoplethysmogram and finger pressure with attention mechanism Scientific Reports |
| title | Deep-learning-based blood pressure estimation using multi channel photoplethysmogram and finger pressure with attention mechanism |
| title_full | Deep-learning-based blood pressure estimation using multi channel photoplethysmogram and finger pressure with attention mechanism |
| title_fullStr | Deep-learning-based blood pressure estimation using multi channel photoplethysmogram and finger pressure with attention mechanism |
| title_full_unstemmed | Deep-learning-based blood pressure estimation using multi channel photoplethysmogram and finger pressure with attention mechanism |
| title_short | Deep-learning-based blood pressure estimation using multi channel photoplethysmogram and finger pressure with attention mechanism |
| title_sort | deep learning based blood pressure estimation using multi channel photoplethysmogram and finger pressure with attention mechanism |
| url | https://doi.org/10.1038/s41598-023-36068-6 |
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