Detection of EEG burst-suppression in neurocritical care patients using an unsupervised machine learning algorithm

•A novel burst suppression detection algorithm that doesn’t require annotated data.•The algorithm adapts to each patient, is fast and provides confidence scores.•We report competitive performance compared to supervised deep neural networks. The burst suppression pattern in clinical electroencephalog...

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Bibliographic Details
Published in:Clinical neurophysiology Vol. 132; no. 10; pp. 2485 - 2492
Main Authors: Narula, G., Haeberlin, M., Balsiger, J., Strässle, C., Imbach, L.L., Keller, E.
Format: Journal Article
Language:English
Published: Elsevier B.V 01.10.2021
Subjects:
Burst suppression
EEG
Machine learning
Neurocritical care
Neurology
Unsupervised
Burst suppression
Unsupervised
Neurocritical care
EEG
Machine learning
machine learning
unsupervised
neurocritical care
ISSN:1388-2457, 1872-8952, 1872-8952
Online Access:Get full text
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Summary:•A novel burst suppression detection algorithm that doesn’t require annotated data.•The algorithm adapts to each patient, is fast and provides confidence scores.•We report competitive performance compared to supervised deep neural networks. The burst suppression pattern in clinical electroencephalographic (EEG) recordings is an important diagnostic tool because of its association with comas of various etiologies, as with hypoxia, drug related intoxication or deep anesthesia. The detection of bursts and the calculation of burst/suppression ratio are often used to monitor the level of anesthesia during treatment of status epilepticus. However, manual counting of bursts is a laborious process open to inter-rater variation and motivates a need for automatic detection. METHODS: We describe a novel unsupervised learning algorithm that detects bursts in EEG and generates burst-per-minute estimates for the purpose of monitoring sedation level in an intensive care unit (ICU). We validated the algorithm on 29 hours of burst annotated EEG data from 29 patients suffering from status epilepticus and hemorrhage. RESULTS: We report competitive results in comparison to neural networks learned via supervised learning. The mean absolute error (SD) in bursts per minute was 0.93 (1.38). CONCLUSION: We present a novel burst suppression detection algorithm that adapts to each patient individually, reports bursts-per-minute quickly, and does not require manual fine-tuning unlike previous approaches to burst-suppression pattern detection. SIGNIFICANCE: Our algorithm for automatic burst suppression quantification can greatly reduce manual oversight in depth of sedation monitoring.
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ISSN:1388-2457
1872-8952
1872-8952
DOI:10.1016/j.clinph.2021.07.018