| 晁静,巩尊科,王世雁,欧香灵,顾成晨,周文欣.脑电图在脑卒中后认知功能评定中的应用[J].中国康复,2024,39(3):150-154 |
| 脑电图在脑卒中后认知功能评定中的应用 |
| Clinical application of electroencephalogram in assessment of cognitive function after stroke |
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| DOI： |
| 中文关键词: 脑卒中 认知障碍 脑电图 频域功率谱分析 洛文斯顿作业疗法 MoCA 认知评估量表 |
| 英文关键词: stroke cognitive impairment electroencephalogram power spectrum analysis Loewenston Occupational Therapy Montreal cognitive scale cognitive assessment scale |
| 基金项目:江苏省卫生健康委员会科研项目(K2019012) |
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| 中文摘要: |
| 目的：探讨脑卒中后认知障碍（PSCI）患者脑电图谱与认知评定量表评分的相关性，分析其临床意义。方法：选取脑卒中患者75例，采用简易精神状态检查量表(MMSE)评定筛查，随机分为认知障碍组(PSCI组)和非认知障碍组(非PSCI组)，每组30例，另选30例健康人为对照组。3组均行脑电图检查、蒙特利尔认知量表(MoCA)及洛文斯顿作业疗法认识评定量表(LOTCA)评定。使用Spearman等级相关分析脑电图特征变化与MoCA、LOTCA评分的相关性，比较3组评定结果。结果：MMSE、MoCA、LOTCA认知量表评分比较，PSCI组明显低于于非PSCI组、健康对照组(P<0.05)，非PSCI组明显低于健康对照组(P<0.05)。αAP和αRP两个脑电指标比较，PSCI组低于非PSCI组和健康对照组(P＜0.05)，非PSCI组低于健康对照组(P<0.05)。DTABR指标比较，健康对照组明显低于非PSCI组、PSCI组(P<0.05)，非PSCI组明显低于PSCI组(P<0.05)。αAP与MoCA、LOTCA之间呈正相关(r=0.734，r=0.922,P<0.05)；αRP与MoCA、LOTCA之间呈正相关(r=0.575,r=0.630,P<0.05)；DTABR与MoCA、LOTCA之间呈负相关(r=-0.569，r=-0.614,P<0.05)。结论：基于频域功率谱分析的脑电数据可作为脑卒中后认知功能的评估办法，且与认知评估量表之间具有相关性，二者结合能更客观全面地评估认知障碍的存在。 |
| 英文摘要: |
| Objective: To investigate the correlation between electroencephalogram (EEG) and cognitive rating scale in patients with cognitive impairment after stroke, and to analyze its clinical significance. Methods: A total of 75 stroke patients were selected and screened using the simple mental state Examination Scale (MMSE). They were randomly divided into cognitive impairment group (PSCI group) and non-cognitive impairment group (non-PSCI group), with 30 cases in each group and 30 healthy subjects served as control group. EEG, Montreal Cognitive Scale (MoCA) and Loewenston Occupational Therapy Cognition Rating Scale (LOTCA) were performed in all three groups. Spearman level correlation was used to analyze the correlation between EEG characteristics and MoCA and LOTCA, and the results of the three groups were compared. Results: The scores of MMSE, MoCA and LOTCA cognitive scale in PSCI group were significantly lower than those in non-PSCI group and healthy control group (P<0.05), while those in non-PSCI group were significantly lower than those in healthy control group (P<0.05). Two EEG indices of αAP and αRP were lower in PSCI group than in non-PSCI group and healthy control group (P<0.05), and lower in non-PSCI group than in healthy control group (P<0.05). DTABR index in healthy control group was significantly lower than in non-PSCI group and PSCI group (P<0.05), and that in non-PSCI group was significantly lower than in PSCI group (P<0.05). αAP was positively correlated with MoCA and LOTCA (r=0.734, r=0.922, P<0.05). αRP was positively correlated with MoCA and LOTCA (r=0.575, r=0.630, P<0.05). DTABR was negatively correlated with MoCA and LOTCA (r=-0.569, r=-0.614, P<0.05). Conclusion: EEG data based on frequency domain power spectrum analysis can be used as an assessment method of cognitive function after stroke, and they are correlated with the cognitive assessment scale. The combination of EEG data and the cognitive assessment scale can evaluate the existence of cognitive impairment more objectively and comprehensively. |
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