An active learning framework for enhancing identification of non-artifactual intracranial pressure waveforms.

ABSTRACT: OBJECTIVE:Intracranial pressure (ICP) is an important and established clinical measurement that is used in the management of severe acute brain injury. ICP waveforms are usually triphasic and are susceptible to artifact because of transient catheter malfunction or routine patient care. Existing methods for artifact detection include threshold-based, stability-based, or template matching, and result in higher false positives (when there is variability in the ICP waveforms) or higher false negatives (when the ICP waveforms lack complete triphasic components but are valid). APPROACH:We hypothesized that artifact labeling of ICP waveforms can be optimized by an active learning approach which includes interactive querying of domain experts to identify a manageable number of informative training

SUBMITTER: Megjhani M 

PROVIDER: S-EPMC6681897 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature