Why the study?
Does a feature-based regression model with artifact removal improve the accuracy of oscillometric blood pressure estimation compared to the conventional maximum amplitude algorithm in healthy subjects?
Population
25 healthy subjects, aged 28 ± 5 years (16 females).
Comparison
Blood pressure estimation using multiple linear… vs Conventional maximum amplitude algorithm method…
Design
Other
Authors
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May reduce SBP estimation error in healthy subjects; leaves open validation in clinical populations before practice consideration.
Does a feature-based regression model with artifact removal improve the accuracy of oscillometric blood pressure estimation compared to the conventional maximum amplitude algorithm in healthy subjects?
A novel feature-based regression approach combined with automated artifact removal significantly improves the accuracy of oscillometric blood pressure measurements compared to conventional maximum amplitude algorithms.
Lim et al. (2015) studied this question.
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