Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
J Neurosci Methods. 2013 Sep 30;219(1):131-41. doi: 10.1016/j.jneumeth.2013.07.003. Epub 2013 Jul 23.
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.
开发一种实时算法，以自动区分危重病成人患者脑电图中的抑制（爆发）和非抑制（爆发）。
提出了一种基于局部电压方差阈值的实时方法，用于将成人 ICU EEG 数据分割为爆发和抑制。结果通过两名经验丰富的人类脑电图专家的手动分割进行验证。我们比较了手动 EEG 分割的专家之间的组内一致性与人类与自动分割之间的组内一致性，并研究了分割质量对算法参数设置变化的稳健性。我们进一步比较了将这些分割作为输入用于计算爆发抑制概率（BSP）的结果，BSP 是抑制深度的连续度量。
自动分割与手动分割相当，即通过比较原始 EEG 分割或衍生的 BSP 信号，算法与人类的一致性与人类与人类的一致性相当。结果对算法参数设置的适度变化具有稳健性。
我们的自动方法在广泛的成人 ICU EEG 模式下令人满意地分割了爆发抑制数据。性能与人类脑电图专家的手动分割相当或超过。
爆发抑制 EEG 模式的自动分割是对危重病和麻醉成人进行定量脑活动监测的重要组成部分。我们算法生成的分割为准确跟踪抑制深度提供了基础。
