An Jingzhi, Jonnalagadda Durga, Moura Valdery, Purdon Patrick L, Brown Emery N, Westover M Brandon
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7430-3. doi: 10.1109/EMBC.2015.7320109.
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.
爆发抑制作为一种控制信号被积极研究，以指导处于药物诱导昏迷状态患者的麻醉剂量。利用爆发抑制概率（BSP）自动识别脑电图抑制期并简洁总结昏迷深度的能力，对于有效且安全地监测和控制药物性昏迷至关重要。然而，当前文献并未明确考虑不同头皮位置的爆发抑制参数的潜在变化。在本研究中，我们分析了8例难治性癫痫持续状态患者的标准19通道脑电图记录，这些患者接受了药物诱导的爆发抑制作为难治性癫痫发作的医学治疗。我们发现，尽管爆发抑制通常被认为是一种全身性现象，但使用先前验证的算法获得的BSP在不同通道间存在系统性变化。我们提出了一种来自各个通道信息的全局表示方法，该方法考虑了在多个电极记录的爆发抑制特征。根据这种代表性爆发抑制模式计算出的BSP可能对噪声更具弹性，并且能更好地反映患者的脑状态。多通道数据整合可能会提高药物性昏迷深度估计的可靠性。
