Alzheimer detection using multivariate decomposition of EEG signals and BDEO feature selection: with lobe-wise and...

In this paper, we propose a multivariate fast iterative filtering (MvFIF) decomposition algorithm, entropy-based features, and a nature-inspired feature selection approach for Alzheimer's disease (AD) detection using electroencephalogram (EEG) signals. Where, the MvFIF decomposes the multichannel EEG signals into multichannel intrinsic mode functions (MIMFs). The entropy features: dispersion entropy (DispEn) and distribution entropy (DistEn) are extracted from the MIMFs. Afterward, five nature-inspired feature selection algorithms are applied to reduce the feature space by selecting the relevant features for the AD. The selected features are finally used for the binary classification to distinguish AD patients from healthy control (HC) subjects using different classifiers. In addition, lobe-wise analysis is performed to understand the neural activity, diagnose AD, and guide targeted treatments. We show that the binary differential evolution optimization (BDEO) feature selection method with the support vector machine (SVM) classifier achieves the highest accuracy of 90.78% with standard deviation (SD) of 1.96% using 10-fold cross-validation (CV) and 75% with SD of 18.20% using leave-one-subject-out CV (LOSO-CV). In lobe-wise analysis, XGBoost classifier with temporal lobe gives the highest accuracy of 80.06% with SD of 1.53% using 10-fold CV and 70.78% with SD of 23.93% using LOSO-CV. The proposed approach surpasses the current leading techniques in AD detection utilizing EEG signals.