文章摘要
七氟醚全麻术后青年和老年患者苏醒期的脑电图类别
Electroencephalography categories in the post anaesthesia care unit between young and elderly patients after sevoflurane general anesthesia
  
DOI：10.12089/jca.2023.03.001
中文关键词: 麻醉恢复室  脑电  聚类算法  老年  脑电图类别
英文关键词: Post anaesthesia care unit  Electroencephalography  Clustering algorithm  Aged  Electroencephalogram categories
基金项目:国家自然科学基金（82030038）
作者单位E-mail
张欣欣 710032,西安市,空军军医大学第一附属医院麻醉与围术期医学科  
李傲 710032,西安市,空军军医大学第一附属医院麻醉与围术期医学科  
刘畑畑 710032,西安市,空军军医大学第一附属医院麻醉与围术期医学科  
杨谦梓 710032,西安市,空军军医大学第一附属医院麻醉与围术期医学科  
董海龙 710032,西安市,空军军医大学第一附属医院麻醉与围术期医学科 hldong6@hotmail.com 
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中文摘要:
      
目的 研究青年和老年患者在接受七氟醚维持的全麻手术后苏醒期的脑电图（EEG）类别。
方法 回顾2018年1月至2020年1月接受非心脏手术且记录围术期全程EEG的患者，根据年龄分为两组：青年组（n=30）和老年组（n=41）。青年组年龄19～38岁，男9例，女21例，ASA Ⅱ或Ⅲ级。老年组年龄65～79岁，男20例，女21例，ASA Ⅱ或Ⅲ级。两组均在完全恢复自主呼吸、咳嗽反射、呼唤可睁眼时拔除气管导管，而后被麻醉科医师送往麻醉恢复室（PACU）观察并记录30 min前额叶EEG信号。采用多窗口谱估计法提取EEG信号中θ、α、β和γ四个频段的功率谱，应用聚类算法寻求四个频段功率谱相似的患者，计算轮廓系数得到PACU中EEG类别最优总数。分析青年组和老年组在各个类别中的数目和四个频段的功率谱大小分布、青年组和老年组特有的EEG类别、PACU中麻醉深度指数（Ai）及θ和γ振荡功率谱在青年组和老年组中的分布特征。
结果 青年组和老年组在PACU的Ai均处于清醒状态区间，青年组的Ai明显高于老年组（P<0.05）。恢复期EEG功率谱显示为6类，青年组30%分布在类别Ⅰ，在类别Ⅵ中没有分布。老年组49%分布在类别Ⅱ中，在类别Ⅰ中没有分布。类别Ⅰ显示最小的θ和α功率，类别Ⅵ显示最小β和γ功率，类别Ⅱ显示较高的θ和α功率，较低的β和γ功率。类别Ⅰ和Ⅵ分别是青年和老年患者特有的类别。青年组87%患者显示高γ功率低θ功率模式，老年组73%患者显示较高θ功率和γ功率的延迟恢复。
结论 青年组和老年组苏醒期的EEG恢复特征存在多个类别，不同的EEG类别代表个体从麻醉中的恢复能力。老年患者苏醒期类别主要表现为较高的θ和α功率，较低的β和γ功率。
英文摘要:
      
Ojective To investigate the electroencephalogram (EEG) categories in the post anaesthesia care unit (PACU) between young and elderly patients after sevoflurane general anesthesia.
Methods This was retrospective cohort study assessing EEG categories in non-cardiac surgical between young and elderly patients from January 2018 to January 2020. According to the age, patients were divided into two groups: young group (n = 30) and elderly group (n = 41). In the young group, there were 9 males and 21 females, aged 19-38 years, ASA physical status Ⅱ or Ⅲ, while in the elderly group, there were 20 males and 21 females, aged 65-79 years, ASA physical status Ⅱ or Ⅲ. The tracheal tube was removed when the patients fully recovered spontaneous breathing, cough reflex, and opened their eyes when they were asked to, and were then sent to the PACU by an anesthesiologist, where the patients' perfrontal EEG signals were recorded for 30 minutes. The multi-window spectral estimation method was used to extract the power spectral of θ, α, β, and γ power, and clustering algorithm were used to find the patients with similar spectral power in the four frequency bands. The countour coefficient was calculated to obtain the optimal total number of EEG categories. The distribution of the number and the four frequency bands, the EEG categories unique to the young and elderly groups, the depth of anesthesia index (Ai) in the PACU and the θ and the γ oscillation power spectrum in the young and elderly groups were analyzed.
Results The Ai of the two groups were both in the awakening range in the recovery room, but the Ai of the young group was slightly higher than the elderly group (P < 0.05). EEG patterns could be characterized into six clusters based on the spectral power of θ, α, β, and γ. 30% of the young group were distributed in category Ⅰ, and none in category Ⅵ. In the elderly group, 49% were distributed in category Ⅱ and none in category Ⅰ. Category Ⅰ and category Ⅵ were characterized by minimal θ and α power or minimal β and γ power, respectively. Category Ⅱ was characterized by higher θ and α power, lower β and γ power. Category Ⅰ was a pattern unique to the young patients, and category Ⅵ was a pattern unique to the elderly patients. 87% young patients possessed a high γ low θ pattern in the PACU, while 73% of the elderly patients intended to sustain a higher θ power rather than the recovery of γ power.
Conclusion There were multiple categories of EEG recovery characteristics in the young and elderly groups, and different EEG categories represent the individual's ability to recover from anesthesia. Elderly patients mainly showed higher θ and α power and lower β and γ power.
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