Improved Confidence-Interval Estimations Using Uncertainty Measure and Weighted Feature Decisions for Cuff-Less Blood-Pressure Measurements
This paper presents a method to improve confidence-interval (CI) estimation using individual uncertainty measures and weighted feature decisions for cuff-less blood-pressure (BP) measurement. We obtained uncertainty using Gaussian process regression (GPR). The CI obtained from the GPR model is compu...
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MDPI AG
2025-01-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/2/131 |
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| author | Soojeong Lee Mugahed A. Al-antari Gyanendra Prasad Joshi |
| author_facet | Soojeong Lee Mugahed A. Al-antari Gyanendra Prasad Joshi |
| author_sort | Soojeong Lee |
| collection | DOAJ |
| description | This paper presents a method to improve confidence-interval (CI) estimation using individual uncertainty measures and weighted feature decisions for cuff-less blood-pressure (BP) measurement. We obtained uncertainty using Gaussian process regression (GPR). The CI obtained from the GPR model is computed using the distribution of BP estimates, which provides relatively wide CIs. Thus, we proposed a method to obtain improved CIs for individual subjects by applying bootstrap and uncertainty methods using the cuff-less BP estimates of each subject obtained through GPR. This study also introduced a novel method to estimate cuff-less BP with high fidelity by determining highly weighted features using weighted feature decisions. The standard deviation of the proposed method’s mean error is 2.94 mmHg and 1.50 mmHg for systolic blood pressure (SBP) and (DBP), respectively. The mean absolute error results were obtained by weighted feature determination combining GPR and gradient boosting algorithms (GBA) for SBP (1.46 mmHg) and DBP (0.69 mmHg). The study confirmed that the BP estimates were within the CI based on the test samples of almost all subjects. The weighted feature decisions combining GPR and GBA were more accurate and reliable for cuff-less BP estimation. |
| format | Article |
| id | doaj-art-c4ebc6d972d34e1085e2bb5a4985a0a6 |
| institution | DOAJ |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Bioengineering |
| spelling | doaj-art-c4ebc6d972d34e1085e2bb5a4985a0a62025-08-20T03:11:58ZengMDPI AGBioengineering2306-53542025-01-0112213110.3390/bioengineering12020131Improved Confidence-Interval Estimations Using Uncertainty Measure and Weighted Feature Decisions for Cuff-Less Blood-Pressure MeasurementsSoojeong Lee0Mugahed A. Al-antari1Gyanendra Prasad Joshi2Department of Computer Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of KoreaDepartment of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of KoreaDepartment of AI Software, Kangwon National University, Kangwon State, Samcheok 10587, Republic of KoreaThis paper presents a method to improve confidence-interval (CI) estimation using individual uncertainty measures and weighted feature decisions for cuff-less blood-pressure (BP) measurement. We obtained uncertainty using Gaussian process regression (GPR). The CI obtained from the GPR model is computed using the distribution of BP estimates, which provides relatively wide CIs. Thus, we proposed a method to obtain improved CIs for individual subjects by applying bootstrap and uncertainty methods using the cuff-less BP estimates of each subject obtained through GPR. This study also introduced a novel method to estimate cuff-less BP with high fidelity by determining highly weighted features using weighted feature decisions. The standard deviation of the proposed method’s mean error is 2.94 mmHg and 1.50 mmHg for systolic blood pressure (SBP) and (DBP), respectively. The mean absolute error results were obtained by weighted feature determination combining GPR and gradient boosting algorithms (GBA) for SBP (1.46 mmHg) and DBP (0.69 mmHg). The study confirmed that the BP estimates were within the CI based on the test samples of almost all subjects. The weighted feature decisions combining GPR and GBA were more accurate and reliable for cuff-less BP estimation.https://www.mdpi.com/2306-5354/12/2/131uncertaintyconfidence intervalscuff-less blood-pressure estimationGaussian processes regressiongradient boosting algorithm |
| spellingShingle | Soojeong Lee Mugahed A. Al-antari Gyanendra Prasad Joshi Improved Confidence-Interval Estimations Using Uncertainty Measure and Weighted Feature Decisions for Cuff-Less Blood-Pressure Measurements Bioengineering uncertainty confidence intervals cuff-less blood-pressure estimation Gaussian processes regression gradient boosting algorithm |
| title | Improved Confidence-Interval Estimations Using Uncertainty Measure and Weighted Feature Decisions for Cuff-Less Blood-Pressure Measurements |
| title_full | Improved Confidence-Interval Estimations Using Uncertainty Measure and Weighted Feature Decisions for Cuff-Less Blood-Pressure Measurements |
| title_fullStr | Improved Confidence-Interval Estimations Using Uncertainty Measure and Weighted Feature Decisions for Cuff-Less Blood-Pressure Measurements |
| title_full_unstemmed | Improved Confidence-Interval Estimations Using Uncertainty Measure and Weighted Feature Decisions for Cuff-Less Blood-Pressure Measurements |
| title_short | Improved Confidence-Interval Estimations Using Uncertainty Measure and Weighted Feature Decisions for Cuff-Less Blood-Pressure Measurements |
| title_sort | improved confidence interval estimations using uncertainty measure and weighted feature decisions for cuff less blood pressure measurements |
| topic | uncertainty confidence intervals cuff-less blood-pressure estimation Gaussian processes regression gradient boosting algorithm |
| url | https://www.mdpi.com/2306-5354/12/2/131 |
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