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

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, Soojin Park

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

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 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 methods: template matching (AUC: 0.71 + 0.04), ICP stability (AUC: 0.51 + 0.036) and threshold-based (AUC: 0.5 + 0.02). Significance: The proposed active learning framework will support real-time ICP-derived analytics by improving precision of artifact-labelling.

Original languageEnglish (US)
Article number015002
JournalPhysiological Measurement
Volume40
Issue number1
DOIs
StatePublished - Jan 17 2019
Externally publishedYes

Keywords

  • active learning
  • artifact cleaning
  • intracranial pressure

ASJC Scopus subject areas

  • Biophysics
  • Physiology
  • Biomedical Engineering
  • Physiology (medical)

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