Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression
Objective. Burst suppression is an electroencephalogram pattern in which bursts of electrical activity alternate with an isoelectric state. This pattern is commonly seen in states of severely reduced brain activity such as profound general anesthesia, anoxic brain injuries, hypothermia and certain d...
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| Format: | Article |
| Language: | en_US |
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IOP Publishing
2014
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| Online Access: | http://hdl.handle.net/1721.1/86317 https://orcid.org/0000-0001-5328-2062 https://orcid.org/0000-0001-5651-5060 https://orcid.org/0000-0003-2668-7819 |
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| author | Chemali, Jessica J. Solt, Ken Purdon, Patrick Lee Brown, Emery Neal Ching, Shinung |
| author2 | Massachusetts Institute of Technology. Institute for Medical Engineering & Science |
| author_facet | Massachusetts Institute of Technology. Institute for Medical Engineering & Science Chemali, Jessica J. Solt, Ken Purdon, Patrick Lee Brown, Emery Neal Ching, Shinung |
| author_sort | Chemali, Jessica J. |
| collection | MIT |
| description | Objective. Burst suppression is an electroencephalogram pattern in which bursts of electrical activity alternate with an isoelectric state. This pattern is commonly seen in states of severely reduced brain activity such as profound general anesthesia, anoxic brain injuries, hypothermia and certain developmental disorders. Devising accurate, reliable ways to quantify burst suppression is an important clinical and research problem. Although thresholding and segmentation algorithms readily identify burst suppression periods, analysis algorithms require long intervals of data to characterize burst suppression at a given time and provide no framework for statistical inference. Approach. We introduce the concept of the burst suppression probability (BSP) to define the brain's instantaneous propensity of being in the suppressed state. To conduct dynamic analyses of burst suppression we propose a state-space model in which the observation process is a binomial model and the state equation is a Gaussian random walk. We estimate the model using an approximate expectation maximization algorithm and illustrate its application in the analysis of rodent burst suppression recordings under general anesthesia and a patient during induction of controlled hypothermia. Main result. The BSP algorithms track burst suppression on a second-to-second time scale, and make possible formal statistical comparisons of burst suppression at different times. Significance. The state-space approach suggests a principled and informative way to analyze burst suppression that can be used to monitor, and eventually to control, the brain states of patients in the operating room and in the intensive care unit. |
| first_indexed | 2024-09-23T10:00:04Z |
| format | Article |
| id | mit-1721.1/86317 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:00:04Z |
| publishDate | 2014 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | mit-1721.1/863172022-09-30T18:12:48Z Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression Chemali, Jessica J. Solt, Ken Purdon, Patrick Lee Brown, Emery Neal Ching, Shinung Massachusetts Institute of Technology. Institute for Medical Engineering & Science Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Ching, ShiNung Purdon, Patrick Lee Solt, Ken Brown, Emery N. Objective. Burst suppression is an electroencephalogram pattern in which bursts of electrical activity alternate with an isoelectric state. This pattern is commonly seen in states of severely reduced brain activity such as profound general anesthesia, anoxic brain injuries, hypothermia and certain developmental disorders. Devising accurate, reliable ways to quantify burst suppression is an important clinical and research problem. Although thresholding and segmentation algorithms readily identify burst suppression periods, analysis algorithms require long intervals of data to characterize burst suppression at a given time and provide no framework for statistical inference. Approach. We introduce the concept of the burst suppression probability (BSP) to define the brain's instantaneous propensity of being in the suppressed state. To conduct dynamic analyses of burst suppression we propose a state-space model in which the observation process is a binomial model and the state equation is a Gaussian random walk. We estimate the model using an approximate expectation maximization algorithm and illustrate its application in the analysis of rodent burst suppression recordings under general anesthesia and a patient during induction of controlled hypothermia. Main result. The BSP algorithms track burst suppression on a second-to-second time scale, and make possible formal statistical comparisons of burst suppression at different times. Significance. The state-space approach suggests a principled and informative way to analyze burst suppression that can be used to monitor, and eventually to control, the brain states of patients in the operating room and in the intensive care unit. National Institutes of Health (U.S.) (Award DP1-OD003646) National Institutes of Health (U.S.) (Award DP2-OD006454) National Institutes of Health (U.S.) (Award K08-GM094394) Burroughs Wellcome Fund (Award 1010625) 2014-05-01T14:06:44Z 2014-05-01T14:06:44Z 2013-09 2012-09 Article http://purl.org/eprint/type/JournalArticle 1741-2560 1741-2552 http://hdl.handle.net/1721.1/86317 Chemali, Jessica, ShiNung Ching, Patrick L Purdon, Ken Solt, and Emery N Brown. “Burst Suppression Probability Algorithms: State-Space Methods for Tracking EEG Burst Suppression.” J. Neural Eng. 10, no. 5 (October 1, 2013): 056017. https://orcid.org/0000-0001-5328-2062 https://orcid.org/0000-0001-5651-5060 https://orcid.org/0000-0003-2668-7819 en_US http://dx.doi.org/10.1088/1741-2560/10/5/056017 Journal of Neural Engineering Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IOP Publishing PMC |
| spellingShingle | Chemali, Jessica J. Solt, Ken Purdon, Patrick Lee Brown, Emery Neal Ching, Shinung Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression |
| title | Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression |
| title_full | Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression |
| title_fullStr | Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression |
| title_full_unstemmed | Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression |
| title_short | Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression |
| title_sort | burst suppression probability algorithms state space methods for tracking eeg burst suppression |
| url | http://hdl.handle.net/1721.1/86317 https://orcid.org/0000-0001-5328-2062 https://orcid.org/0000-0001-5651-5060 https://orcid.org/0000-0003-2668-7819 |
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