Why the study?
PPG-based continuous blood pressure estimators have shown promise, but prior methods were often validated on small cohorts with moderate blood pressure variations.
Does a PPG morphology feature-based machine learning approach accurately estimate blood pressure during anesthesia induction compared to an invasive reference?
Population
Subjects undergoing anesthesia induction
Comparison
Three machine learning methods vs invasive reference and previous oBPM technology
Design
Validation and comparative algorithm evaluation study
Authors
Loading...
May improve non-invasive SBP tracking during anesthesia induction; leaves open clinical adoption pending outcome trials.
Does a PPG morphology feature-based machine learning approach accurately estimate blood pressure during anesthesia induction compared to an invasive reference?
A machine learning approach using PPG morphology features accurately tracks blood pressure variations during anesthesia induction, significantly improving estimation accuracy over previous non-invasive technology.
Aguet et al. (2023) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: