| Title |
Uncertainty in photoplethysmography-based cuffless blood pressure trend monitoring: a personalized approach |
| Authors |
Rinkevičius, Mantas ; Charlton, Peter H ; Lukoševičius, Arūnas ; Marozas, Vaidotas |
| DOI |
10.22489/CinC.2024.098 |
| Full Text |
|
| Is Part of |
Computing in cardiology (CinC): [51st international conference, Karlsruhe, Germany, 8-11 September 2024].. Piscataway, NJ : IEEE. 2024, vol. 51, ID 98, p. 1-4.. ISSN 2325-8861. eISSN 2325-887X |
| Abstract [eng] |
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. |
| Published |
Piscataway, NJ : IEEE |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2024 |
| CC license |
|
