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Spatial variation in automated burst suppression detection in pharmacologically induced coma

机译:药理学诱导昏迷中自动突发抑制检测的空间变化

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摘要

Burst suppression is actively studied as a control signal to guide anesthetic dosing in patients undergoing medically induced coma. The ability to automatically identify periods of EEG suppression and compactly summarize the depth of coma using the burst suppression probability (BSP) is crucial to effective and safe monitoring and control of medical coma. Current literature however does not explicitly account for the potential variation in burst suppression parameters across different scalp locations. In this study we analyzed standard 19-channel EEG recordings from 8 patients with refractory status epilepticus who underwent pharmacologically induced burst suppression as medical treatment for refractory seizures. We found that although burst suppression is generally considered a global phenomenon, BSP obtained using a previously validated algorithm varies systematically across different channels. A global representation of information from individual channels is proposed that takes into account the burst suppression characteristics recorded at multiple electrodes. BSP computed from this representative burst suppression pattern may be more resilient to noise and a better representation of the brain state of patients. Multichannel data integration may enhance the reliability of estimates of the depth of medical coma.
机译:积极研究猝发抑制作为控制信号,以指导接受药物诱发的昏迷患者的麻醉剂量。自动识别脑电图抑制周期并使用爆发抑制概率(BSP)紧凑总结昏迷深度的能力对于有效,安全地监控医学昏迷至关重要。然而,当前文献没有明确地说明跨不同头皮位置的猝发抑制参数的潜在变化。在这项研究中,我们分析了8例难治性癫痫持续状态患者的标准19通道EEG记录,这些患者接受了药理学上的猝发抑制作为难治性癫痫的药物治疗。我们发现,虽然通常认为突发抑制是一种全局现象,但是使用先前验证的算法获得的BSP在不同通道之间系统地变化。考虑到在多个电极处记录的猝发抑制特性,提出了来自各个通道的信息的全局表示。根据这种代表性的猝发抑制模式计算出的BSP可能对噪声更有弹性,并且可以更好地代表患者的大脑状态。多通道数据集成可以增强对医学昏迷深度的估计的可靠性。

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