Towards automated EEG-Based Alzheimer's disease diagnosis using relevance vector machines

Abstract

Abstract-Existing electroencephalography (EEG) basedAlzheimer's disease (AD) diagnostic systems typically rely on experts to visually inspect and segment the collected signals into artefact-free epochs and on support vector machine (SVM) based classifiers. The manual selection process, however, introduces biases and errors into the diagnostic procedure, renders it "semiautomated," and makes the procedure costly and labour-intensive. In this paper, we overcome these limitations by proposing the use of an automated artefact removal (AAR) algorithm to remove artefacts from the EEG signal without the need for human intervention. We investigate the effects of the so-called waveletenhanced independent component analysis (wICA) AAR on three classes of EEG features, namely spectral power, coherence, and amplitude modulation, and ultimately, on diagnostic accuracy, specificity and sensitivity. Furthermore, we propose to replace the binary SVM classifier with a soft-decision relevance vector machine (RVM) classifier. Experimental results show the proposed RVM-based system outperforming the SVM trained on features extracted from both manually-selected and wICAprocessed epochs. Moreover, the class membership information output by the RVM is shown to provide clinicians with a richer pool of information to assist with AD assessment.

Multimodal Prediction of Alzheimer's Disease Severity Level Based on Resting-State EEG and Structural MRI

“…In an attempt to match the dimensionality reported in previous EEG-based AD literature (e.g., Falk et al, 2012 ; Fraga et al, 2013 ; Cassani et al, 2014b ), the top 24 features from each of the feature modalities was selected. Here, we call feature “Group 1” the top-24 features selected for the EEG modality.…”

Early diagnosis of mild cognitive impairment and Alzheimer’s with event-related potentials and event-related desynchronization in N-back working memory tasks

“…The literature on EEG or MEG use in assisting AD diagnosis is clearly 30 divided into two main approaches [8,9,10]. The first one deals with EEG or MEG signals registered when participants are awake at rest, with eyes open or closed (resting-state) [11,12,13,14,15,16], while the other is dedicated to the analysis of signals recorded with subjects performing some pre-defined tasks (task-oriented) [17,18,19,20,21]. Both paradigms can be analyzed in time and 35 frequency domains, bringing information about cognitive functions related to the characteristics of brain signals [22,23,10].…”

EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques

“…Kanda et al ( 2014 ) achieved an accuracy of up to 84.56% in classification. In another work, Cassani et al ( 2014 ) estimated three EEG signal features: spectral, coherence, and amplitude modulation, and then utilized a SVM to obtain an accuracy of 84.7%. Upon comparing the outcomes of our study with those of Cura et al ( 2021 ) study, we observed that our study outperformed theirs.…”

“…achieved an accuracy of up to 84.56% in classification. In another work,Cassani et al (2014) estimated three EEG signal features: spectral, coherence, and amplitude modulation, and then utilized a SVM to obtain an accuracy ofTABLE Classification precision, recall, and accuracy for neurotypical vs. mild and moderate AD features based on k-fold cross-validation techniques. The bold values represent the highest classification precision, recall, and accuracy achieved by each classifier.…”