Feasibility of Unobtrusively Estimating Blood Pressure Using Load Cells under the Legs of a Bed
The ability to monitor blood pressure unobtrusively and continuously, even during sleep, may promote the prevention of cardiovascular diseases, enable the early detection of cardiovascular risk, and facilitate the timely administration of treatment. Publicly available data from forty participants co...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/1/96 |
| _version_ | 1797358219130044416 |
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| author | Gary Garcia-Molina |
| author_facet | Gary Garcia-Molina |
| author_sort | Gary Garcia-Molina |
| collection | DOAJ |
| description | The ability to monitor blood pressure unobtrusively and continuously, even during sleep, may promote the prevention of cardiovascular diseases, enable the early detection of cardiovascular risk, and facilitate the timely administration of treatment. Publicly available data from forty participants containing synchronously recorded signals from four force sensors (load cells located under each leg of a bed) and continuous blood pressure waveforms were leveraged in this research. The focus of this study was on using a deep neural network with load-cell data as input composed of three recurrent layers to reconstruct blood pressure (BP) waveforms. Systolic (SBP) and diastolic (DBP) blood pressure values were estimated from the reconstructed BP waveform. The dataset was partitioned into training, validation, and testing sets, such that the data from a given participant were only used in a single set. The BP waveform reconstruction performance resulted in an R<sup>2</sup> of 0.61 and a mean absolute error < 0.1 mmHg. The estimation of the mean SBP and DBP values was characterized by Bland–Altman-derived limits of agreement in intervals of [−11.99 to 15.52 mmHg] and [−7.95 to +3.46 mmHg], respectively. These results may enable the detection of abnormally large or small variations in blood pressure, which indicate cardiovascular health degradation. The apparent contrast between the small reconstruction error and the limit-of-agreement width owes to the fact that reconstruction errors manifest more prominently at the maxima and minima, which are relevant for SBP and DBP estimation. While the focus here was on SBD and DBP estimation, reconstructing the entire BP waveform enables the calculation of additional hemodynamic parameters. |
| first_indexed | 2024-03-08T14:57:43Z |
| format | Article |
| id | doaj.art-c44ba71f0fda4fe288177c7d9f6fdd4b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T14:57:43Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c44ba71f0fda4fe288177c7d9f6fdd4b2024-01-10T15:08:33ZengMDPI AGSensors1424-82202023-12-012419610.3390/s24010096Feasibility of Unobtrusively Estimating Blood Pressure Using Load Cells under the Legs of a BedGary Garcia-Molina0Sleep Number Labs, San Jose, CA 95113, USAThe ability to monitor blood pressure unobtrusively and continuously, even during sleep, may promote the prevention of cardiovascular diseases, enable the early detection of cardiovascular risk, and facilitate the timely administration of treatment. Publicly available data from forty participants containing synchronously recorded signals from four force sensors (load cells located under each leg of a bed) and continuous blood pressure waveforms were leveraged in this research. The focus of this study was on using a deep neural network with load-cell data as input composed of three recurrent layers to reconstruct blood pressure (BP) waveforms. Systolic (SBP) and diastolic (DBP) blood pressure values were estimated from the reconstructed BP waveform. The dataset was partitioned into training, validation, and testing sets, such that the data from a given participant were only used in a single set. The BP waveform reconstruction performance resulted in an R<sup>2</sup> of 0.61 and a mean absolute error < 0.1 mmHg. The estimation of the mean SBP and DBP values was characterized by Bland–Altman-derived limits of agreement in intervals of [−11.99 to 15.52 mmHg] and [−7.95 to +3.46 mmHg], respectively. These results may enable the detection of abnormally large or small variations in blood pressure, which indicate cardiovascular health degradation. The apparent contrast between the small reconstruction error and the limit-of-agreement width owes to the fact that reconstruction errors manifest more prominently at the maxima and minima, which are relevant for SBP and DBP estimation. While the focus here was on SBD and DBP estimation, reconstructing the entire BP waveform enables the calculation of additional hemodynamic parameters.https://www.mdpi.com/1424-8220/24/1/96load cellsbedblood pressurewaveform reconstructionunobtrusive sensingdeep learning |
| spellingShingle | Gary Garcia-Molina Feasibility of Unobtrusively Estimating Blood Pressure Using Load Cells under the Legs of a Bed Sensors load cells bed blood pressure waveform reconstruction unobtrusive sensing deep learning |
| title | Feasibility of Unobtrusively Estimating Blood Pressure Using Load Cells under the Legs of a Bed |
| title_full | Feasibility of Unobtrusively Estimating Blood Pressure Using Load Cells under the Legs of a Bed |
| title_fullStr | Feasibility of Unobtrusively Estimating Blood Pressure Using Load Cells under the Legs of a Bed |
| title_full_unstemmed | Feasibility of Unobtrusively Estimating Blood Pressure Using Load Cells under the Legs of a Bed |
| title_short | Feasibility of Unobtrusively Estimating Blood Pressure Using Load Cells under the Legs of a Bed |
| title_sort | feasibility of unobtrusively estimating blood pressure using load cells under the legs of a bed |
| topic | load cells bed blood pressure waveform reconstruction unobtrusive sensing deep learning |
| url | https://www.mdpi.com/1424-8220/24/1/96 |
| work_keys_str_mv | AT garygarciamolina feasibilityofunobtrusivelyestimatingbloodpressureusingloadcellsunderthelegsofabed |