Real-time segmentation of burst suppression patterns in critical care EEG monitoring.
Real-time segmentation of burst suppression patterns in critical care EEG monitoring.
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DOI：
10.1016/j.jneumeth.2013.07.003
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发表时间：
2013-09-30
影响因子：
3
通讯作者：
Brown, Emery N.
中科院分区：
文献类型：
--
作者：
Westover, M. Brandon;Shafi, Mouhsin M.;Ching, ShiNung;Chemali, Jessica J.;Purdon, Patrick L.;Cash, Sydney S.;Brown, Emery N.
开发一种实时算法，以自动区分重症成人患者脑电图中的Supponent和Non-Supponent（突发）。提出了一种基于局部电压方差阈值的成人ICU脑电数据突发和突发的实时分割方法。两个经验丰富的人类脑电图对手动分割的结果进行验证。我们比较人工EEG分割专家与人与自动分割之间的评分员间协议之间的评分员间协议，并研究分割质量对算法参数设置变化的鲁棒性。我们进一步比较了使用这些分割作为输入计算突发抑制概率（BSP），抑制深度的连续测量的结果。自动分割与手动分割相当，即算法与人类的一致性与人类与人类的一致性相当，通过比较原始EEG分割或推导的BSP信号进行判断。结果对算法参数设置的适度变化具有稳健性。我们的自动化方法令人满意地分割爆发抑制数据在广泛的成人ICU EEG模式。性能相当于或超过人类脑电图学家的手动分割。爆发抑制EEG模式的自动分割是危重患者和麻醉成人定量脑活动监测的重要组成部分。我们的算法产生的分割提供了一个基础，精确跟踪的抑制深度。
Develop a real-time algorithm to automatically discriminate suppressions from non-suppressions (bursts) in electroencephalograms of critically ill adult patients. 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. 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. 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. 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.
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影响因子：
3.8
作者：
Hallberg, B.;Grossmann, K.;Blennow, M.
通讯作者：
Blennow, M.
DOI：
10.1007/bf02332690
发表时间：
1995-01-01
期刊：
INTERNATIONAL JOURNAL OF CLINICAL MONITORING AND COMPUTING
影响因子：
--
作者：
LIPPING, T;JANTTI, V;HARTIKAINEN, K
通讯作者：
HARTIKAINEN, K
DOI：
10.1007/bf02025311
发表时间：
1996-01-01
期刊：
JOURNAL OF CLINICAL MONITORING
影响因子：
--
作者：
Arnold, M;Doering, A;Eisel, M
通讯作者：
Eisel, M
影响因子：
9.8
作者：
Doyle, PW;Matta, BF
通讯作者：
Matta, BF
DOI：
10.1073/pnas.1121461109
发表时间：
2012-02-21
影响因子：
11.1
作者：
Ching, ShiNung;Purdon, Patrick L.;Brown, Emery N.
通讯作者：
Brown, Emery N.