1. An active learning framework for enhancing identification of non-artifactual intracranial pressure waveforms
- Author
-
Brendan K. Wallace, Xiao Hu, Raghav Padmanabhan, Sachin Agarwal, E. Sander Connolly, Clio Rubinos, David Roh, Jan Claassen, Soojin Park, Kalijah Terilli, Jenna Ford, Ayham Alkhachroum, Julie Kromm, and Murad Megjhani more...
- Subjects
Male ,Intracranial Pressure ,Physiology ,Computer science ,Active learning (machine learning) ,0206 medical engineering ,Biomedical Engineering ,Biophysics ,Stability (learning theory) ,02 engineering and technology ,Article ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Text mining ,Physiology (medical) ,False positive paradox ,Humans ,False Positive Reactions ,Intracranial pressure ,Artifact (error) ,integumentary system ,business.industry ,musculoskeletal, neural, and ocular physiology ,Template matching ,Signal Processing, Computer-Assisted ,Pattern recognition ,Middle Aged ,020601 biomedical engineering ,humanities ,nervous system diseases ,Identification (information) ,Brain Injuries ,Female ,Artificial intelligence ,Artifacts ,business ,030217 neurology & neurosurgery - 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. more...
- Published
- 2019
- Full Text
- View/download PDF