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.

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].…”

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.…”

Aphid Identification and Counting Based on Smartphone and Machine Vision

“…Cassani et al approached automated EEG-based Alzheimer's disease diagnosis using relevance vector machines [2], Chakraborty and Newton studied climate change, plant diseases, and food security [3], Pal and Foody used evaluation of SVM, RVM, and SMLR for accurate image classification with limited ground data [4], Patel et al used improved multiple features based algorithm for fruit detection [5], Luo et al develop an aphid damage hyperspectral index for detecting aphid (Hemiptera: Aphididae) damage levels in winter wheat and so on [6].…”