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Assessment of Non-Invasive Blood Pressure Prediction From PPG and rPPG Signals Using Deep Learning
2021
Sensors
Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error
doi:10.3390/s21186022
pmid:34577227
pmcid:PMC8472879
fatcat:jpiv4pb2zraa3hcdaixkwq3rna