Fleischmann Antonia, Pilge Stefanie, Kiel Tobias, Kratzer Stephan, Schneider Gerhard, Kreuzer Matthias
Department of Anesthesiology and Intensive Care, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany.
Front Hum Neurosci. 2018 Sep 21;12:368. doi: 10.3389/fnhum.2018.00368. eCollection 2018.
Different anesthetic agents induce burst suppression in the electroencephalogram (EEG) at very deep levels of general anesthesia. EEG burst suppression has been identified to be a risk factor for postoperative delirium (POD). EEG based automated detection algorithms are used to detect burst suppression patterns during general anesthesia and a burst suppression ratio (BSR) is calculated. Unfortunately, applied algorithms do not give information as precisely as suggested, often resulting in an underestimation of the patients' burst suppression level. Additional knowledge of substance-specific burst suppression patterns could be of great importance to improve the ability of EEG based monitors to detect burst suppression. In a re-analysis of EEG recordings obtained from a previous study, we analyzed EEG data of 45 patients undergoing elective surgery under general anesthesia. The patients were anesthetized with sevoflurane, isoflurane or propofol ( = 15, for each group). After skin incision, the used agent was titrated to a level when burst suppression occurred. In a visual analysis of the EEG, blinded to the used anesthetic agent, we included the first distinct burst in our analysis. To avoid bias through changing EEG dynamics throughout the burst, we only focused on the first 2 s of the burst. These episodes were analyzed using the power spectral density (PSD) and normalized PSD, the absolute burst amplitude and absolute burst slope, as well as permutation entropy (PeEn). Our results show significant substance-specific differences in the architecture of the burst. Volatile-induced bursts showed higher burst amplitudes and higher burst power. Propofol-induced bursts had significantly higher relative power in the EEG alpha-range. Further, isoflurane-induced bursts had the steepest burst slopes. We can present the first systematic comparison of substance-specific burst characteristics during anesthesia. Previous observations, mostly derived from animal studies, pointing out the substance-specific differences in bursting behavior, concur with our findings. Our findings of substance-specific EEG characteristics can provide information to help improve automated burst suppression detection in monitoring devices. More specific detection of burst suppression may be helpful to reduce excessive EEG effects of anesthesia and therefore the incidence of adverse outcomes such as POD.
不同的麻醉剂在全身麻醉极深水平时会在脑电图（EEG）中诱发爆发抑制。脑电图爆发抑制已被确定为术后谵妄（POD）的一个风险因素。基于脑电图的自动检测算法用于在全身麻醉期间检测爆发抑制模式，并计算爆发抑制率（BSR）。不幸的是，应用的算法提供的信息并不像所建议的那样精确，常常导致对患者爆发抑制水平的低估。物质特异性爆发抑制模式的额外知识对于提高基于脑电图的监测器检测爆发抑制的能力可能非常重要。在对先前一项研究获得的脑电图记录进行重新分析时，我们分析了45例接受全身麻醉下择期手术患者的脑电图数据。患者分别用七氟醚、异氟醚或丙泊酚麻醉（每组n = 15）。皮肤切开后，将所用药物滴定至出现爆发抑制的水平。在对脑电图进行视觉分析时，对所用麻醉剂不知情，我们在分析中纳入了第一个明显的爆发。为避免因爆发期间脑电图动态变化产生偏差，我们只关注爆发的前2秒。使用功率谱密度（PSD）和归一化PSD、绝对爆发幅度和绝对爆发斜率以及排列熵（PeEn）对这些片段进行分析。我们的结果显示，爆发结构存在显著的物质特异性差异。挥发性药物诱发的爆发具有更高的爆发幅度和更高的爆发功率。丙泊酚诱发的爆发在脑电图α范围内具有显著更高的相对功率。此外，异氟醚诱发的爆发具有最陡的爆发斜率。我们可以首次对麻醉期间物质特异性爆发特征进行系统比较。先前的观察结果大多来自动物研究，指出了爆发行为的物质特异性差异，与我们的发现一致。我们关于物质特异性脑电图特征的发现可以提供信息，以帮助改进监测设备中自动爆发抑制检测。更特异性地检测爆发抑制可能有助于减少麻醉对脑电图的过度影响，从而降低诸如POD等不良后果的发生率。
