A Full-Stack Application for Detecting Seizures and Reducing Data During Continuous Electroencephalogram Monitoring.

Autor:	Bernabei JM; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA.; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA., Owoputi O; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA.; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA., Small SD; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA.; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA., Nyema NT; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA.; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA., Dumenyo E; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA.; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA., Kim J; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA.; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA., Baldassano SN; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA.; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA., Painter C; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA.; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA., Conrad EC; Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA., Ganguly TM; Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA., Balu R; Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA., Davis KA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA.; Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA., Levine JM; Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA., Pathmanathan J; Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA., Litt B; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA.; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA.; Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA.; Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA.
Jazyk:	angličtina
Zdroj:	Critical care explorations [Crit Care Explor] 2021 Jul 13; Vol. 3 (7), pp. e0476. Date of Electronic Publication: 2021 Jul 13 (Print Publication: 2021).
DOI:	10.1097/CCE.0000000000000476
Abstrakt:	Continuous electroencephalogram monitoring is associated with lower mortality in critically ill patients; however, it is underused due to the resource-intensive nature of manually interpreting prolonged streams of continuous electroencephalogram data. Here, we present a novel real-time, machine learning-based alerting and monitoring system for epilepsy and seizures that dramatically reduces the amount of manual electroencephalogram review. Methods: We developed a custom data reduction algorithm using a random forest and deployed it within an online cloud-based platform, which streams data and communicates interactively with caregivers via a web interface to display algorithm results. We developed real-time, machine learning-based alerting and monitoring system for epilepsy and seizures on continuous electroencephalogram recordings from 77 patients undergoing routine scalp ICU electroencephalogram monitoring and tested it on an additional 20 patients. Results: We achieved a mean seizure sensitivity of 84% in cross-validation and 85% in testing, as well as a mean specificity of 83% in cross-validation and 86% in testing, corresponding to a high level of data reduction. This study validates a platform for machine learning-assisted continuous electroencephalogram analysis and represents a meaningful step toward improving utility and decreasing cost of continuous electroencephalogram monitoring. We also make our high-quality annotated dataset of 97 ICU continuous electroencephalogram recordings public for others to validate and improve upon our methods. (Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)
Databáze:	MEDLINE
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