Real-time segmentation of burst suppression patterns in critical care EEG monitoring
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Brown_Real-time segmentation.pdf
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Author(s)
Shafi, Mouhsin M.
Ching, ShiNung
Chemali, Jessica J.
Cash, Sydney S.
Brown, Emery N.
Westover, M. Brandon
Purdon, Patrick Lee
Date Issued
July 2013
Journal
Journal of Neuroscience Methods
Publisher
Elsevier
Citation
Brandon Westover, M., Mouhsin M. Shafi, ShiNung Ching, Jessica J. Chemali, Patrick L. Purdon, Sydney S. Cash, and Emery N. Brown. “Real-Time Segmentation of Burst Suppression Patterns in Critical Care EEG Monitoring.” Journal of Neuroscience Methods 219, no. 1 (September 2013): 131–141.
Version
Author's final manuscript
Abstract
Objective
Develop a real-time algorithm to automatically discriminate suppressions from non-suppressions (bursts) in electroencephalograms of critically ill adult patients.
Methods
A real-time method for segmenting adult ICU EEG data into bursts and suppressions is presented based on thresholding local voltage variance. Results are validated against manual segmentations by two experienced human electroencephalographers. We compare inter-rater agreement between manual EEG segmentations by experts with inter-rater agreement between human vs automatic segmentations, and investigate the robustness of segmentation quality to variations in algorithm parameter settings. We further compare the results of using these segmentations as input for calculating the burst suppression probability (BSP), a continuous measure of depth-of-suppression.
Results
Automated segmentation was comparable to manual segmentation, i.e. algorithm-vs-human agreement was comparable to human-vs-human agreement, as judged by comparing raw EEG segmentations or the derived BSP signals. Results were robust to modest variations in algorithm parameter settings.
Conclusions
Our automated method satisfactorily segments burst suppression data across a wide range adult ICU EEG patterns. Performance is comparable to or exceeds that of manual segmentation by human electroencephalographers.
Significance
Automated segmentation of burst suppression EEG patterns is an essential component of quantitative brain activity monitoring in critically ill and anesthetized adults. The segmentations produced by our algorithm provide a basis for accurate tracking of suppression depth.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Picower Institute for Learning and Memory
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DOI of Published Version
https://doi.org/10.1016/j.jneumeth.2013.07.003
