Dept. of Psychiatry, Harbor-UCLA Medical Center, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine at UCLA, United States; Department of Psychiatry and Biobehvioral Sciences, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine at UCLA, United States.
Greenwings Biomedical, Irvine, United States.
J Clin Neurosci. 2020 Jun;76:195-200. doi: 10.1016/j.jocn.2020.04.003. Epub 2020 Apr 16.
Alzheimer's disease and mild cognitive impairment are increasingly prevalent global health concerns in aging industrialized societies. There are only limited non-invasive biomarkers for the cognitive and functional impairment associated with dementia. Multifractal analysis of EEG has recently been proposed as having the potential to be an improved method of quantitative EEG analysis compared to existing techniques (e.g., spectral analysis). We utilized an existing database of a study of healthy elderly patients (N = 20) who were assessed with cognitive testing (Folstein Mini Mental Status Exam; MMSE) and resting state EEG (4 leads). Each subject's EEG was separated into two 30 s tracings for training and testing a statistical model against the MMSE scores. We compared multifractal detrended fluctuation analysis (MF-DFA) against Fourier Transform (FT) in the ability to produce an accurate classification and regression trees estimator for the testing EEG segments. The MF-DFA-based statistical model MMSE estimation strongly correlated with the actual MMSE when applied to the test EEG parameter dataset, whereas the corresponding FT-based model did not. Using a standardized cutoff value for MMSE-based clinical staging, the MF-DFA-based statistical model was both sensitive and specific for clinical staging of both mild Alzheimer's disease and mild cognitive impairment. MF-DFA shows promise as a method of quantitative EEG analysis to accurately estimate cognition in Alzheimer's disease.
阿尔茨海默病和轻度认知障碍是老龄化工业化社会日益关注的全球健康问题。目前只有有限的非侵入性生物标志物可用于诊断与痴呆相关的认知和功能障碍。与现有的技术（例如频谱分析）相比，脑电多分数分析最近被提出具有成为定量脑电分析改进方法的潜力。我们利用了一项针对健康老年患者的研究的现有数据库（N=20），这些患者接受了认知测试（Folstein 迷你精神状态检查；MMSE）和静息状态脑电（4 导）的评估。每位受试者的脑电被分为两个 30 秒的记录，用于根据 MMSE 分数训练和测试统计模型。我们比较了多重分形去趋势波动分析（MF-DFA）与傅里叶变换（FT）在产生准确分类和回归树估计器方面的能力，用于测试脑电段。当应用于测试脑电参数数据集时，基于 MF-DFA 的统计模型 MMSE 估计与实际 MMSE 强烈相关，而相应的基于 FT 的模型则没有。使用基于 MMSE 的临床分期的标准化截止值，基于 MF-DFA 的统计模型对轻度阿尔茨海默病和轻度认知障碍的临床分期具有敏感性和特异性。MF-DFA 有望成为一种定量脑电分析方法，可准确估计阿尔茨海默病患者的认知能力。
