EEG-Based Classification Between Individuals with Mild Cognitive Impairment and Healthy Controls Using Conformal Kernel-Based Fuzzy Support Vector Machine

Dementia due to Alzheimer's disease has become a global health burden. Early detection of one of its potential prodromal state, mild cognitive impairment (MCI), appears to be critical for early interventions. The present study, therefore, addressed the challenging issue of classifying individuals with MCI vs. healthy controls (HC) based on resting-state electroencephalography (EEG). Specifically, we design a feature extraction and feature fusion framework to extract discriminative features. In the first step, we extracted the sample entropy (SampEn) and Katz's fractal dimension (KFD) features from their corresponding best electrodes selected by a wrapper-based algorithm. Next, the two best feature sets were fused by a principal component analysis (PCA) method. The eigen-complexity pattern (ECP) feature extracted by this framework was subsequently fed into the proposed MCI detector, the conformal kernel-based fuzzy support vector machine (CKF-SVM). The CKF-SVM not only adopts a fuzzified penalty strategy to avoid the overfitting often observed in conventional SVM due to outliers, but also applies a conformally transformed kernel to further increase the class separability. The results carried out on 51 participants (24 MCI, 27 HC) and leave-one-participant-out cross-validation (LOPO-CV) show that the ECP feature combined with a simple linear discriminant analysis classifier achieved an accuracy of 84.31%, higher than the one by either of the two complexity measures (SampEn and KFD), as well as the ones by spectral powers of different scalp regions. The results also show that CKF-SVM outperformed SVM and other classifiers commonly used in the MCI-HC classification studies. A high LOPO-CV accuracy 90.19% (sensitivity = 87.50%, specificity = 92.60%) was achieved by using the ECP feature and the CKF-SVM classifier. These results suggested that the proposed approach has a potential for developing a sensitive EEG-based computer-aided diagnosis (CAD) system that may, in the future, provide an objective measure to assist physicians' diagnose of MCI and even a neurofeedback brain-computer interface (BCI) system for monitoring the intervention response of individuals with MCI.