A Robust Discriminant Framework Based on Functional Biomarkers of EEG and Its Potential for Diagnosis of Alzheimer's Disease.
A Robust Discriminant Framework Based on Functional Biomarkers of EEG and Its Potential for Diagnosis of Alzheimer's Disease.
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基于脑电图功能生物标志物的鲁棒判别框架及其诊断阿尔茨海默病的潜力
DOI：
10.3390/healthcare8040476
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发表时间：
2020-11-11
期刊：
影响因子：
--
通讯作者：
Zhang JX
中科院分区：
文献类型：
--
作者：
Ge Q;Lin ZC;Gao YX;Zhang JX
(1)背景资料：越来越多的证据表明，记录大脑电活动的脑电图（EEG）可以成为阿尔茨海默病（AD）的一种有前途的诊断工具。基于定量脑电（qEEG）的诊断生物标志物已被广泛探索，但很少能在日常实践中帮助临床医生，而且仍然缺乏可靠的qEEG标志物。该研究旨在寻找强大的EEG生物标志物，并提出一个基于信号处理和计算机辅助技术的系统识别框架，以区分AD患者和正常老年人对照（NC）。(2)研究方法：首先对脑电信号进行预处理，然后对预处理后的脑电信号进行最大重叠离散小波变换。方差，皮尔逊相关系数，四分位距，Hoeffding的D测量，和排列熵被提取作为候选分类器的输入。评估了每个模型的AD与NC判别性能，并最终开发了自动诊断框架。(3)结果如下：基于脑电特征提取的分类器和基于线性判别分析的分类器分别获得了93.18 ± 3.65（%）、97.92 ± 1.66（%）和94.06 ± 4.04（%）的准确率。(4)结论：所开发的判别框架可以在系统常规中以高性能从NC中识别AD。
(1) Background: Growing evidence suggests that electroencephalography (EEG), recording the brain’s electrical activity, can be a promising diagnostic tool for Alzheimer’s disease (AD). The diagnostic biomarkers based on quantitative EEG (qEEG) have been extensively explored, but few of them helped clinicians in their everyday practice, and reliable qEEG markers are still lacking. The study aims to find robust EEG biomarkers and propose a systematic discrimination framework based on signal processing and computer-aided techniques to distinguish AD patients from normal elderly controls (NC). (2) Methods: In the proposed study, EEG signals were preprocessed firstly and Maximal overlap discrete wavelet transform (MODWT) was applied to the preprocessed signals. Variance, Pearson correlation coefficient, interquartile range, Hoeffding’s D measure, and Permutation entropy were extracted as the input of the candidate classifiers. The AD vs. NC discriminant performance of each model was evaluated and an automatic diagnostic framework was eventually developed. (3) Results: A classification procedure based on the extracted EEG features and linear discriminant analysis based classifier achieved the accuracy of 93.18 ± 3.65 (%), the AUC of 97.92 ± 1.66 (%), the F-measure of 94.06 ± 4.04 (%), separately. (4) Conclusions: The developed discrimination framework can identify AD from NC with high performance in a systematic routine.
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影响因子：
4.8
作者：
Cassani R;Falk TH;Fraga FJ;Kanda PA;Anghinah R
通讯作者：
Anghinah R
DOI：
10.1016/j.jalz.2011.03.005
发表时间：
2011-05
期刊：
Alzheimer's & dementia : the journal of the Alzheimer's Association
影响因子：
--
作者：
McKhann GM;Knopman DS;Chertkow H;Hyman BT;Jack CR Jr;Kawas CH;Klunk WE;Koroshetz WJ;Manly JJ;Mayeux R;Mohs RC;Morris JC;Rossor MN;Scheltens P;Carrillo MC;Thies B;Weintraub S;Phelps CH
通讯作者：
Phelps CH
影响因子：
14
作者：
通讯作者：
--
影响因子：
20.6
作者：
BODENSTEIN, G;PRAETORIUS, HM
通讯作者：
PRAETORIUS, HM
DOI：
10.1016/j.jalz.2011.03.004
发表时间：
2011-05
期刊：
Alzheimer's & dementia : the journal of the Alzheimer's Association
影响因子：
--
作者：
Jack CR Jr;Albert MS;Knopman DS;McKhann GM;Sperling RA;Carrillo MC;Thies B;Phelps CH
通讯作者：
Phelps CH