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Classification of EEG Bursts in Deep Sevoflurane, Desflurane and Isoflurane Anesthesia Using AR-modeling and Entropy Measures

机译:使用Ar型和熵措施对深七氟醚,脱氟醚和异氟醚麻醉进行脑电图的分类

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摘要

A study relating signal patterns of burst onsets in burst suppression EEG to the anesthetic agent or anesthesia induction protocol is presented. A dataset of 82 recordings of sevofiurane, isoflurane and desflurane anesthesia underlies the study. 3 second segments from the onset of altogether 3214 bursts are described using AR model parameters, spectral entropy and sample entropy as features. The features are clustered using the K-means algorithm. The results indicate that no clear cut distinction can be made between the burst patterns induced by the mentioned anesthetics although bursts of certain properties are more common in certain patient groups. Several directions for further investigations are proposed based on visual inspection of the recordings.
机译:介绍了突发抑制EEG中突发胰腺囊的信号模式的研究表达到麻醉剂或麻醉感应方案。 研究的82个录像的数据集,异氟醚和Desfluane麻醉下降了这项研究。 使用AR模型参数,光谱熵和样本熵作为特征,描述了来自完全3214突发的突发发作的3个第二段。 使用K-Means算法集群集群。 结果表明,由于某些性质的突发在某些患者基团中更常见,因此不能在突发突发模式之间进行清晰的切割区分。 基于录音的目视检查,提出了几个进一步调查的方向。

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