Uncertainty in Photoplethysmography-Based Cuffless Blood Pressure Trend Monitoring: A Personalized Approach

Cuffless blood pressure (BP) monitoring using photoplethysmography (PPG) is increasingly being integrated into wearables to track conditions such as hypertension.However, rather high uncertainty of continuous BP monitoring, mainly related with inter-personal variations, limits effective applications.This study aims to propose a personalized PPG analysis-based method for estimating cuffless BP and providing its uncertainty bounds.The PulseDB Vital database consisting of 2938 subjects was used.A Gaussian Process Regression model was implemented to estimate systolic and diastolic BP from PPG morphological features.Based on feature ranking, the pulse duration was found to be the most dominant predictor.The proportion of subjects with a mean absolute error < 5 mmHg was 28.80% and 60.72% for systolic and diastolic BP, respectively.The study demonstrated that the proposed approach has the potential to estimate trends of cuffless BP, especially of diastolic BP, and its prediction uncertainty.

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Publication Details

Journal
Computing in cardiology
Published
2024-12-01
DOI
https://doi.org/10.22489/cinc.2024.098
Primary Topic
Non-Invasive Vital Sign Monitoring
Type
article
Field-Weighted Citation Impact
0.00

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article

Uncertainty in Photoplethysmography-Based Cuffless Blood Pressure Trend Monitoring: A Personalized Approach

Vaidotas Marozas, Mantas Rinkevičius, Peter Charlton
Computing in cardiology
Non-Invasive Vital Sign Monitoring
article

Uncertainty in Photoplethysmography-Based Cuffless Blood Pressure Trend Monitoring: A Personalized Approach

Vaidotas Marozas, Mantas Rinkevičius, Peter Charlton
article en

Abstract

Cuffless blood pressure (BP) monitoring using photoplethysmography (PPG) is increasingly being integrated into wearables to track conditions such as hypertension.However, rather high uncertainty of continuous BP monitoring, mainly related with inter-personal variations, limits effective applications.This study aims to propose a personalized PPG analysis-based method for estimating cuffless BP and providing its uncertainty bounds.The PulseDB Vital database consisting of 2938 subjects was used.A Gaussian Process Regression model was implemented to estimate systolic and diastolic BP from PPG morphological features.Based on feature ranking, the pulse duration was found to be the most dominant predictor.The proportion of subjects with a mean absolute error < 5 mmHg was 28.80% and 60.72% for systolic and diastolic BP, respectively.The study demonstrated that the proposed approach has the potential to estimate trends of cuffless BP, especially of diastolic BP, and its prediction uncertainty.

Computing in cardiologyVol. 51
European Association of National Metrology Institutes
Openalex Percentile: Top 79%
Non-Invasive Vital Sign Monitoring
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