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Novel Burst Suppression Segmentation in the Joint Time-Frequency Domain for EEG in Treatment of Status Epilepticus

机译:联合时频域中的新型突发抑制分割脑电图在癫痫持续状态的治疗中

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摘要

We developed a method to distinguish bursts and suppressions for EEG burst suppression from the treatments of status epilepticus, employing the joint time-frequency domain. We obtained the feature used in the proposed method from the joint use of the time and frequency domains, and we estimated the decision as to whether the measured EEG was a burst segment or suppression segment by the maximum likelihood estimation. We evaluated the performance of the proposed method in terms of its accordance with the visual scores and estimation of the burst suppression ratio. The accuracy was higher than the sole use of the time or frequency domains, as well as conventional methods conducted in the time domain. In addition, probabilistic modeling provided a more simplified optimization than conventional methods. Burst suppression quantification necessitated precise burst suppression segmentation with an easy optimization; therefore, the excellent discrimination and the easy optimization of burst suppression by the proposed method appear to be beneficial.
机译:我们开发了一种方法,采用联合时频域来区分癫痫持续状态的猝发和抑制与癫痫持续状态的治疗。我们从时域和频域的联合使用中获得了所提出方法中使用的特征,并通过最大似然估计来估计关于测得的EEG是突发段还是抑制段的决策。我们根据视觉评分和突发抑制率的估计,评估了该方法的性能。准确性高于仅使用时域或频域以及在时域中执行的常规方法。此外,与常规方法相比,概率建模提供了更为简化的优化。突发抑制量化需要精确的突发抑制分段和简单的优化;因此,通过所提出的方法的出色的判别力和容易的突发抑制优化似乎是有益的。

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