Artifacts annotations in anesthesia blood pressure data by man and machine.

ABSTRACT: Physiologic data from anesthesia monitors are automatically captured. Yet erroneous data are stored in the process as well. While this is not interfering with clinical care, research can be affected. Researchers should find ways to remove artifacts. The aim of the present study was to compare different artifact annotation strategies, and to assess if a machine learning algorithm is able to accept or reject individual data points. Non-cardiac procedures requiring invasive blood pressure monitoring were eligible. Two trained research assistants observed procedures live for artifacts. The same procedures were also retrospectively annotated for artifacts by a different person. We compared the different ways of artifact identifications and modelled artifacts with three different learning algori

SUBMITTER: Pasma W 

PROVIDER: S-EPMC7943498 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature