An active learning framework for enhancing identification of non-artifactual intracranial pressure waveforms
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 examples. Main results : The resulting active learning based framework identified
non-artifactual ICP pulses with a superior AUC of 0.96 + 0.012, compared to existing me...
Source: Physiological Measurement - Category: Physiology Authors: Murad Megjhani, Ayham Alkhachroum, Kalijah Terilli, Jenna Ford, Clio Rubinos, Julie Kromm, Brendan K Wallace, E Sander Connolly, David Roh, Sachin Agarwal, Jan Claassen, Raghav Padmanabhan, Xiao Hu and Soojin Park Source Type: research