Automatic EEG signal classification for epilepsy diagnosis with Relevance Vector Machines

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dc.contributor.authorLima C.A.M.
dc.contributor.authorCoelho A.L.V.
dc.contributor.authorChagas S.
dc.date.accessioned2024-03-13T01:34:33Z
dc.date.available2024-03-13T01:34:33Z
dc.date.issued2009
dc.description.abstractIn this paper, we investigate the potentials of applying a kernel-based learning machine, the Relevance Vector Machine (RVM), to the task of epilepsy detection through automatic electroencephalogram (EEG) signal classification. For this purpose, some experiments have been conducted over publicly available data, contrasting the performance levels exhibited by RVM models with those achieved with Support Vector Machines (SVMs), both in terms of predictive accuracy and sensitivity to the choice of the kernel function. Four settings of both types of kernel machine were considered in this study, which vary in accord with the type of input data they receive, either raw EEG signal or some statistical features extracted from the wavelet-transformed data. The empirical results indicate that: (1) in terms of accuracy, the best-calibrated RVM models have shown very satisfactory performance levels, which are rather comparable to those of SVMs; (2) an increase of accuracy is sometimes accompanied by loss of sparseness in the resulting RVM models; (3) both types of machines present similar sensitivity profiles to the kernel functions considered, having some kernel parameter values clearly associated with better accuracy rate; (4) when not making use of a feature extraction technique, the choice of the kernel function seems to be very relevant for significantly leveraging the performance of RVMs; and (5) when making use of derived features, the choice of the feature extraction technique seems to be an important factor to one take into account. © 2009 Elsevier Ltd. All rights reserved.
dc.description.firstpage10054
dc.description.issuenumber6
dc.description.lastpage10059
dc.description.volume36
dc.identifier.doi10.1016/j.eswa.2009.01.022
dc.identifier.issn0957-4174
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/37353
dc.relation.ispartofExpert Systems with Applications
dc.rightsAcesso Restrito
dc.subject.otherlanguageEEG signal classification
dc.subject.otherlanguageEpilepsy
dc.subject.otherlanguageKernel machines
dc.subject.otherlanguageSensitivity analysis
dc.titleAutomatic EEG signal classification for epilepsy diagnosis with Relevance Vector Machines
dc.typeArtigo
local.scopus.citations77
local.scopus.eid2-s2.0-64449084585
local.scopus.subjectAccuracy rates
local.scopus.subjectEEG signal classification
local.scopus.subjectEEG signals
local.scopus.subjectElectro-encephalogram signals
local.scopus.subjectEmpirical results
local.scopus.subjectEpilepsy
local.scopus.subjectEpilepsy detections
local.scopus.subjectFeature extraction techniques
local.scopus.subjectInput datum
local.scopus.subjectKernel functions
local.scopus.subjectKernel machines
local.scopus.subjectKernel parameters
local.scopus.subjectKernel-based learning
local.scopus.subjectPerformance levels
local.scopus.subjectPredictive accuracies
local.scopus.subjectRelevance vector machines
local.scopus.subjectSensitivity profiles
local.scopus.subjectStatistical features
local.scopus.subjectSupport vectors
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=64449084585&origin=inward
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