Spatial variation in automated burst suppression detection in pharmacologically induced coma
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Author(s)
Jonnalagadda, Durga
Moura, Valdery
Purdon, Patrick L.
Westover, M. Brandon
An, Jingzhi
Brown, Emery Neal
Date Issued
November 2015
Journal
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Jingzhi An, et al. “Spatial Variation in Automated Burst Suppression Detection in Pharmacologically Induced Coma.” 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) August 25-29 2015, Milan, Italy, Institute of Electrical and Electronics Engineers (IEEE), November 2015 © 2015 Institute of Electrical and Electronics Engineers (IEEE)
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Author's final manuscript
Abstract
Burst suppression is actively studied as a control signal to guide anesthetic dosing in patients undergoing medically induced coma. The ability to automatically identify periods of EEG suppression and compactly summarize the depth of coma using the burst suppression probability (BSP) is crucial to effective and safe monitoring and control of medical coma. Current literature however does not explicitly account for the potential variation in burst suppression parameters across different scalp locations. In this study we analyzed standard 19-channel EEG recordings from 8 patients with refractory status epilepticus who underwent pharmacologically induced burst suppression as medical treatment for refractory seizures. We found that although burst suppression is generally considered a global phenomenon, BSP obtained using a previously validated algorithm varies systematically across different channels. A global representation of information from individual channels is proposed that takes into account the burst suppression characteristics recorded at multiple electrodes. BSP computed from this representative burst suppression pattern may be more resilient to noise and a better representation of the brain state of patients. Multichannel data integration may enhance the reliability of estimates of the depth of medical coma.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/EMBC.2015.7320109
