Mou Shaoqi, Yan Shiyu, Shen Shanhong, Shuai Yibin, Li Gang, Shen Zhongxia, Shen Ping
Department of Psychiatry, Wenzhou Medical University, Wenzhou, People's Republic of China.
Department of Sleep Medical Center, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, People's Republic of China.
Neuropsychiatr Dis Treat. 2024 Jul 18;20:1409-1419. doi: 10.2147/NDT.S458106. eCollection 2024.
Anxiety disorder (AD) is a common disabling disease. The prolonged disease course may lead to impaired cognitive performance, brain function, and a bad prognosis. Few studies have examined the effect of disease course on brain function by electroencephalogram (EEG).
Resting-state EEG analysis was performed in 34 AD patients. The 34 patients with AD were divided into two groups according to the duration of their illness: anxious state (AS) and generalized anxiety disorder (GAD). Then, EEG features, including univariate power spectral density (PSD), fuzzy entropy (FE), and multivariable functional connectivity (FC), were extracted and compared between AS and GAD. These features were evaluated by three previously validated machine learning methods to test the accuracy of classification in AS and GAD.
Significant decreased PSD and FE in GAD were detected compared with AS, especially in the Alpha 2 band. In addition, FC analysis indicated that GAD patients' connection between the left and right hemispheres decreased. Based on machine learning, AS and GAD are classified on a six-month criterion with the highest classification accuracy of up to 0.99 ± 0.0015.
The brain function of patients is more severely impaired in AD patients with longer illness duration. Resting-state EEG demonstrated to be a promising examination in the classification in GAD and AS using machine learning methods with better classification accuracy.
焦虑症(AD)是一种常见的致残性疾病。病程延长可能导致认知能力、脑功能受损及预后不良。很少有研究通过脑电图(EEG)来考察病程对脑功能的影响。
对34例AD患者进行静息态EEG分析。根据病程将34例AD患者分为两组:焦虑状态(AS)组和广泛性焦虑障碍(GAD)组。然后,提取并比较AS组和GAD组的EEG特征,包括单变量功率谱密度(PSD)、模糊熵(FE)和多变量功能连接(FC)。通过三种先前经验证的机器学习方法对这些特征进行评估,以测试AS组和GAD组分类的准确性。
与AS组相比,GAD组的PSD和FE显著降低,尤其是在α2频段。此外,FC分析表明GAD患者左右半球之间的连接减少。基于机器学习,以六个月为标准对AS组和GAD组进行分类,最高分类准确率可达0.99±0.0015。
病程较长的AD患者脑功能受损更严重。静息态EEG在使用机器学习方法对GAD和AS进行分类时显示出良好的前景,分类准确率更高。