Subject-Based Model for Reconstructing Arterial Blood Pressure from Photoplethysmogram
The continuous prediction of arterial blood pressure (ABP) waveforms via non-invasive methods is of great significance for the prevention and treatment of cardiovascular disease. Photoplethysmography (PPG) can be used to reconstruct ABP signals due to having the same excitation source and high signa...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/9/8/402 |
| _version_ | 1827600726331752448 |
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| author | Qunfeng Tang Zhencheng Chen Rabab Ward Carlo Menon Mohamed Elgendi |
| author_facet | Qunfeng Tang Zhencheng Chen Rabab Ward Carlo Menon Mohamed Elgendi |
| author_sort | Qunfeng Tang |
| collection | DOAJ |
| description | The continuous prediction of arterial blood pressure (ABP) waveforms via non-invasive methods is of great significance for the prevention and treatment of cardiovascular disease. Photoplethysmography (PPG) can be used to reconstruct ABP signals due to having the same excitation source and high signal similarity. The existing methods of reconstructing ABP signals from PPG only focus on the similarities between systolic, diastolic, and mean arterial pressures without evaluating their global similarity. This paper proposes a deep learning model with a W-Net architecture to reconstruct ABP signals from PPG. The W-Net consists of two concatenated U-Net architectures, the first acting as an encoder and the second as a decoder to reconstruct ABP from PPG. Five hundred records of different lengths were used for training and testing. The experimental results yielded high values for the similarity measures between the reconstructed ABP signals and their reference ABP signals: the Pearson correlation, root mean square error, and normalized dynamic time warping distance were 0.995, 2.236 mmHg, and 0.612 mmHg on average, respectively. The mean absolute errors of the SBP and DBP were 2.602 mmHg and 1.450 mmHg on average, respectively. Therefore, the model can reconstruct ABP signals that are highly similar to the reference ABP signals. |
| first_indexed | 2024-03-09T04:41:14Z |
| format | Article |
| id | doaj.art-aa6b23fe4fdc45fe814ad29534098956 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-09T04:41:14Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-aa6b23fe4fdc45fe814ad295340989562023-12-03T13:20:31ZengMDPI AGBioengineering2306-53542022-08-019840210.3390/bioengineering9080402Subject-Based Model for Reconstructing Arterial Blood Pressure from PhotoplethysmogramQunfeng Tang0Zhencheng Chen1Rabab Ward2Carlo Menon3Mohamed Elgendi4School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, ChinaDepartment of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z2, CanadaBiomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, 8008 Zurich, SwitzerlandDepartment of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z2, CanadaThe continuous prediction of arterial blood pressure (ABP) waveforms via non-invasive methods is of great significance for the prevention and treatment of cardiovascular disease. Photoplethysmography (PPG) can be used to reconstruct ABP signals due to having the same excitation source and high signal similarity. The existing methods of reconstructing ABP signals from PPG only focus on the similarities between systolic, diastolic, and mean arterial pressures without evaluating their global similarity. This paper proposes a deep learning model with a W-Net architecture to reconstruct ABP signals from PPG. The W-Net consists of two concatenated U-Net architectures, the first acting as an encoder and the second as a decoder to reconstruct ABP from PPG. Five hundred records of different lengths were used for training and testing. The experimental results yielded high values for the similarity measures between the reconstructed ABP signals and their reference ABP signals: the Pearson correlation, root mean square error, and normalized dynamic time warping distance were 0.995, 2.236 mmHg, and 0.612 mmHg on average, respectively. The mean absolute errors of the SBP and DBP were 2.602 mmHg and 1.450 mmHg on average, respectively. Therefore, the model can reconstruct ABP signals that are highly similar to the reference ABP signals.https://www.mdpi.com/2306-5354/9/8/402digital healthdata scienceintensive and critical carecardiologyelectrocadiogramvital sign analysis |
| spellingShingle | Qunfeng Tang Zhencheng Chen Rabab Ward Carlo Menon Mohamed Elgendi Subject-Based Model for Reconstructing Arterial Blood Pressure from Photoplethysmogram Bioengineering digital health data science intensive and critical care cardiology electrocadiogram vital sign analysis |
| title | Subject-Based Model for Reconstructing Arterial Blood Pressure from Photoplethysmogram |
| title_full | Subject-Based Model for Reconstructing Arterial Blood Pressure from Photoplethysmogram |
| title_fullStr | Subject-Based Model for Reconstructing Arterial Blood Pressure from Photoplethysmogram |
| title_full_unstemmed | Subject-Based Model for Reconstructing Arterial Blood Pressure from Photoplethysmogram |
| title_short | Subject-Based Model for Reconstructing Arterial Blood Pressure from Photoplethysmogram |
| title_sort | subject based model for reconstructing arterial blood pressure from photoplethysmogram |
| topic | digital health data science intensive and critical care cardiology electrocadiogram vital sign analysis |
| url | https://www.mdpi.com/2306-5354/9/8/402 |
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