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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Bibliographic Details
Published in:Bioengineering (Basel) Vol. 12; no. 2; p. 131
Main Authors: Lee, Soojeong, Al-antari, Mugahed A., Joshi, Gyanendra Prasad
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01.02.2025
MDPI
Subjects:
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
gradient boosting algorithm
Gaussian processes regression
confidence intervals
uncertainty
cuff-less blood-pressure estimation
ISSN:2306-5354, 2306-5354
Online Access:Get full text
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Summary: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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ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering12020131