Zheng Xiaowei, Wang Bozhi, Liu Hao, Wu Wencan, Sun Jiamin, Fang Wei, Jiang Rundong, Hu Yajie, Jin Cheng, Wei Xin, Chen Steve Shyh-Ching
Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China.
School of Mathematics, Northwest University, Xian, China.
Front Aging Neurosci. 2023 Nov 7;15:1288295. doi: 10.3389/fnagi.2023.1288295. eCollection 2023.
Alzheimer's disease (AD) is the most common neurogenerative disorder, making up 70% of total dementia cases with a prevalence of more than 55 million people. Electroencephalogram (EEG) has become a suitable, accurate, and highly sensitive biomarker for the identification and diagnosis of AD.
In this study, a public database of EEG resting state-closed eye recordings containing 36 AD subjects and 29 normal subjects was used. And then, three types of signal features of resting-state EEG, i.e., spectrum, complexity, and synchronization, were performed by applying various signal processing and statistical methods, to obtain a total of 18 features for each signal epoch. Next, the supervised machine learning classification algorithms of decision trees, random forests, and support vector machine (SVM) were compared in categorizing processed EEG signal features of AD and normal cases with leave-one-person-out cross-validation.
The results showed that compared to normal cases, the major change in EEG characteristics in AD cases was an EEG slowing, a reduced complexity, and a decrease in synchrony. The proposed methodology achieved a relatively high classification accuracy of 95.65, 95.86, and 88.54% between AD and normal cases for decision trees, random forests, and SVM, respectively, showing that the integration of spectrum, complexity, and synchronization features for EEG signals can enhance the performance of identifying AD and normal subjects.
This study recommended the integration of EEG features of spectrum, complexity, and synchronization for aiding the diagnosis of AD.
阿尔茨海默病（AD）是最常见的神经退行性疾病，占痴呆病例总数的70%，全球患病率超过5500万人。脑电图（EEG）已成为用于AD识别和诊断的合适、准确且高度敏感的生物标志物。
在本研究中，使用了一个包含36名AD受试者和29名正常受试者的静息状态闭眼EEG记录公共数据库。然后，通过应用各种信号处理和统计方法，对静息状态EEG的三种信号特征，即频谱、复杂度和同步性进行分析，每个信号时段共获得18个特征。接下来，采用决策树、随机森林和支持向量机（SVM）等监督机器学习分类算法，通过留一法交叉验证对AD和正常病例的处理后EEG信号特征进行分类比较。
结果表明，与正常病例相比，AD病例的EEG特征主要变化为EEG减慢、复杂度降低和同步性下降。所提出的方法在决策树、随机森林和SVM中，AD与正常病例之间的分类准确率分别达到了相对较高的95.65%、95.86%和88.54%，表明EEG信号的频谱、复杂度和同步性特征的整合可以提高AD和正常受试者识别的性能。
本研究建议整合EEG的频谱、复杂度和同步性特征以辅助AD的诊断。
