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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| Published in: | Bioengineering (Basel) Vol. 12; no. 2; p. 131 |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Joshi, Gyanendra Prasad Lee, Soojeong Al-antari, Mugahed A. |
| AuthorAffiliation | 1 Department of Computer Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea; leesoo86@sejong.ac.kr 2 Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea 3 Department of AI Software, Kangwon National University, Kangwon State, Samcheok 10587, Republic of Korea |
| AuthorAffiliation_xml | – name: 1 Department of Computer Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea; leesoo86@sejong.ac.kr – name: 2 Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea – name: 3 Department of AI Software, Kangwon National University, Kangwon State, Samcheok 10587, Republic of Korea |
| Author_xml | – sequence: 1 givenname: Soojeong surname: Lee fullname: Lee, Soojeong – sequence: 2 givenname: Mugahed A. orcidid: 0000-0002-4457-4407 surname: Al-antari fullname: Al-antari, Mugahed A. – sequence: 3 givenname: Gyanendra Prasad orcidid: 0000-0002-5446-288X surname: Joshi fullname: Joshi, Gyanendra Prasad |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40001651$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1109/TCE.2022.3174689 10.1161/01.CIR.101.23.e215 10.1109/LSENS.2023.3285135 10.1109/JTEHM.2022.3209754 10.1016/j.bspc.2024.106860 10.1093/eurheartj/ehac432 10.1093/abm/16.3.210 10.1161/HYP.0000000000000067 10.3390/diagnostics12112886 10.1016/j.bspc.2024.106662 10.1049/htl.2016.0100 10.1161/HYPERTENSIONAHA.117.10237 10.7551/mitpress/3206.001.0001 10.1155/2018/7804243 10.1038/s41598-024-66514-y 10.1038/s41598-022-22205-0 10.1016/j.bspc.2023.105287 10.3390/app12168265 10.7763/IJCTE.2017.V9.1138 10.1109/TBME.2016.2580904 10.1016/j.compbiomed.2019.103392 10.1016/j.aej.2021.04.035 10.1109/ACCESS.2017.2787980 10.3390/sym13040686 10.1109/JBHI.2024.3395445 10.1016/j.jcmds.2024.100102 10.1109/MIM.2002.1005654 10.1214/aos/1013203451 10.21037/atm.2017.03.34 10.4304/jcp.7.1.161-168 10.1016/j.compbiomed.2015.11.008 10.1109/TIM.2011.2161926 10.1109/ACCESS.2023.3234597 10.3390/info15070394 10.3390/diagnostics13040736 10.1109/EMBC48229.2022.9871777 10.1038/s41440-022-01123-8 10.1109/SMARTCOMP.2016.7501681 10.2307/2530926 10.1016/j.bspc.2020.101942 10.1097/HJH.0000000000003483 10.1142/S0219720005001004 10.1016/j.bspc.2021.103404 10.1016/j.bspc.2024.106741 10.1109/JBHI.2021.3128383 10.1038/sj.jhh.1001683 10.1007/978-3-319-68415-4_1 10.1109/ICoSNIKOM60230.2023.10364417 10.3390/s20072108 10.1109/JBHI.2022.3172514 |
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| Keywords | gradient boosting algorithm Gaussian processes regression confidence intervals uncertainty cuff-less blood-pressure estimation |
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| SubjectTerms | Algorithms Blood pressure Confidence intervals cuff-less blood-pressure estimation Decisions Electrocardiography Estimates Exercise Gaussian process Gaussian processes regression gradient boosting algorithm Hypertension Measurement Monitoring systems Neighborhoods Neural networks Physiology Statistical analysis Uncertainty |
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| Title | Improved Confidence-Interval Estimations Using Uncertainty Measure and Weighted Feature Decisions for Cuff-Less Blood-Pressure Measurements |
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