A Robust Discriminant Framework Based on Functional Biomarkers of EEG and Its Potential for Diagnosis of Alzheimer’s Disease

Abstract

(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.

Electroencephalography as a Non-Invasive Biomarker of Alzheimer’s Disease: A Forgotten Candidate to Substitute CSF Molecules?

“…Even with the small sample size, they showed that a single fractal measure could discriminate between AD subjects and controls with a 67% sensitivity and a specificity of 99.9%. Similarly, in a recent study, Ge et al [ 140 ] proposed a framework that systematically discriminates among AD patients and age-matched controls based on EEG signal processing. Combining several EEG features, they obtained a ROC curve with an area under the curve of 97.92 ± 1.66 (%).…”

Motor imagery EEG signal classification with a multivariate time series approach

“…As can be seen in Fig. 1 , with a linear discriminant using 55 variables or more, a classification accuracy of 100% is obtained using the d4 wavelet, although with 50 variables this same wavelet reaches results close to 100%, improving the results reported in [ 21 ]. Regarding the other wavelets examined, we can see how all of them, except for Haar, achieve an accuracy between 98% and 100% using 40 variables, while with 45 variables all of them exceed 99% of accuracy.…”

“…In 2005, Kannathal et al applied it to EEG signals [ 18 ]. Other relevant work along this line include the classification of EEG signals in binary groups by means of standard artificial neural networks to discriminate between normal or epileptic individuals [ 19 ], the use of a cross-correlation based feature extractor aided with a support vector machine classifier for emotional speech recognition [ 20 ], or the use of wavelets for a diagnostic tool for Alzheimer’s disease [ 21 ]. Some works developing general adaptive methods are based on weighted-distance nearest-neighbor classifiers [ 22 ], the Discrete Wavelet Transform (DWT) based feature extraction schemes [ 23 ], and multi-trial EEG clustering [ 24 ].…”

Comparison of Smoothing Filters’ Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain–Computer Interface Headset during Audio Stimulation

“…As it was mentioned above, analysis of biomedical data, in particular EEG, is a very challenging task [ 9 , 10 , 15 , 35 ]. Having a large number of EEG channels makes the whole data analysis process more complex, which can be eased by inter alia selection of only those necessary channels [ 37 , 38 , 39 , 40 ]. It also allows reducing the set up time [ 37 , 41 ].…”