Emergence EEG pattern classification in sevoflurane anesthesia
Emergence EEG pattern classification in sevoflurane anesthesia
复制标题
七氟烷麻醉中的苏醒脑电图模式分类
DOI：
10.1088/1361-6579/aab4d0
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发表时间：
2018-04-01
影响因子：
3.2
通讯作者：
Bai, Yang
中科院分区：
文献类型：
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作者：
Liang, Zhenhu;Huang, Cheng;Bai, Yang
目的：在全麻后意识恢复过程中，个体患者存在显著的频谱脑电图（EEG）模式特征。然而，这些EEG模式不能使用市售的麻醉深度（DoA）监测器定量识别。本研究提出了一种有效的分类方法和指标来分类患者之间的这些模式。方法：根据来自两家医院的52名接受七氟醚全身麻醉的患者的EEG数据集，确定了四种类型的苏醒EEG模式。然后，选择临床感兴趣的五个频率子带（δ、θ、α、β和γ）的相对功率谱密度（RPSD）用于出现状态分析。最后，采用遗传算法支持向量机（GA-SVM）对脑电模式进行识别。性能报告的灵敏度（SE），特异性（SP）和准确性（AC）。主要结果：在GA-SVM分类中，δ和α波段中RPSD的均值和众数（P（δ）/P（α））的组合表现最好。GA-SVM得到的AC指数分别为90.64 ± 7.61、81.79 ± 5.84、82.14 ± 7.99和72.86 ± 11.11。此外，EEG出现模式I和III的患者的出现时间随着患者年龄的增加而增加。而对于EEG出现模式IV的患者，当患者年龄小于50岁时，出现时间与患者年龄呈正相关，当患者年龄大于50岁时，出现时间与患者年龄呈负相关。意义：P（delta）/P（alpha）的均值和众数是区分不同类型脑电图的有用指标。此外，这些模式可能与患者年龄相关的潜在神经基质相关。γ-氨基丁酸（GABA）能麻醉药出现4种脑电图模式。提出了一种结合遗传算法和支持向量机的脑电模式识别方法。⑶采用相对功率谱密度（RPSD）作为特征对脑电模式进行分类，取得了较好的分类效果。统计结果表明，苏醒期脑电图模式与年龄有关，可能对术后脑状态的评估有价值。
Objective: Significant spectral electroencephalogram (EEG) pattern characteristics exist in individual patients during the re-establishment of consciousness after general anesthesia. However, these EEG patterns cannot be quantitatively identified using commercially available depth of anesthesia (DoA) monitors. This study proposes an effective classification method and indices to classify these patterns among patients. Approach: Four types of emergence EEG patterns were identified based on the EEG data set from 52 patients undergoing sevoflurane general anesthesia from two hospitals. Then, the relative power spectrum density (RPSD) of five frequency sub-bands of clinical interest (delta, theta, alpha, beta and gamma) were selected for emergence state analysis. Finally, a genetic algorithm support vector machine (GA-SVM) was used to identify the emergence EEG patterns. The performance was reported in terms of sensitivity (SE), specificity (SP) and accuracy (AC). Main results: The combination of the mean and mode of RPSD in the delta and alpha band (P (delta)/P (alpha) performed the best in the GA-SVM classification. The AC indices obtained by GA-SVM across the four patterns were 90.64 ± 7.61, 81.79 ± 5.84, 82.14 ± 7.99 and 72.86 ± 11.11 respectively. Furthermore, the emergence time of the patients with EEG emergence patterns I and III increased as the patients’ age increased. However, for patients with EEG emergence pattern IV, the emergence time positively correlates with the patients’ age when they are under 50, and negatively correlates with it when they are over 50. Significance: The mean and mode of P (delta)/P (alpha) is a useful index to classify the different emergence EEG patterns. In addition, these patterns may correlate with an underlying neural substrate which is related to the patients’ age. Highlights ► Four emergence EEG patterns were found in γ-amino-butyric acid (GABA)-ergic anesthetic drugs. ► A genetic algorithm combined with a support vector machine (GA-SVM) was proposed to identify the emergence EEG patterns. ► The relative power spectrum density (RPSD) was used as a feature to classify the emergence EEG patterns and good accuracy was achieved. ► The statistics shows that the emergence EEG patterns are age-related and may have value in assessing postoperative brain states.