Towards Automated EEG-Based Alzheimer's Disease Diagnosis Using Relevance Vector Machines
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Citações na Scopus
Tipo de produção
conferenceObject
Data de publicação
2014
Título da Revista
ISSN da Revista
Título do Volume
Editora
IEEE
Autores
Citação
5TH ISSNIP-IEEE BIOSIGNALS AND BIOROBOTICS CONFERENCE (2014): BIOSIGNALS AND ROBOTICS FOR BETTER AND SAFER LIVING, p.112-117, 2014
Resumo
Existing electroencephalography (EEG) based Alzheimer'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 ""semi-automated,"" 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 wavelet-enhanced 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 wICA-processed 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.
Palavras-chave
Alzheimer's disease, electroencephalography, support vector machine (SVM), relevance vector machine (RVM), wICA
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