Shi Yupan, Ma Qinying, Feng Chunyu, Wang Mingwei, Wang Hualong, Li Bing, Fang Jiyu, Ma Shaochen, Guo Xin, Li Tongliang
Institute of Applied Mathematics, Hebei Academy of Sciences, Shijiazhuang, China.
Hebei Authentication Technology Engineering Research Center, Shijiazhuang, China.
Health Inf Sci Syst. 2022 Jul 26;10(1):16. doi: 10.1007/s13755-022-00186-8. eCollection 2022 Dec.
Electroencephalogram (EEG) microstates provide powerful tools for identifying EEG features due to their rich temporal information. In this study, we tested whether microstates can measure the severity of Alzheimer's disease (AD) and mild cognitive impairment (MCI) in patients and effectively distinguish AD from MCI. We defined two features using transition probabilities (), and one was used to evaluate between-group differences in microstate parameters to assess the within-group consistency of and MMSE scores. Another feature was used to distinguish AD from MCI in machine learning models. Tests showed that there were between-group differences in the temporal characteristics of microstates, and some kinds of were significantly correlated with MMSE scores within groups. Based on our newly defined time-factor transition probabilities () feature and partial accumulation strategy, we obtained promising scores for accuracy, sensitivity, and specificity of 0.938, 0.923, and 0.947, respectively. These results provide evidence for microstates as a neurobiological marker of AD.
脑电图（EEG）微状态因其丰富的时间信息，为识别EEG特征提供了强大工具。在本研究中，我们测试了微状态是否能够测量患者中阿尔茨海默病（AD）和轻度认知障碍（MCI）的严重程度，并有效区分AD和MCI。我们使用转移概率定义了两个特征，其中一个用于评估微状态参数的组间差异，以评估转移概率和简易精神状态检查表（MMSE）分数的组内一致性。另一个特征用于在机器学习模型中区分AD和MCI。测试表明，微状态的时间特征存在组间差异，并且某些转移概率与组内MMSE分数显著相关。基于我们新定义的时间因素转移概率特征和部分累积策略，我们分别获得了准确率、灵敏度和特异性的良好分数，分别为0.938、0.923和0.947。这些结果为微状态作为AD的神经生物学标志物提供了证据。
