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A hybrid feature selection approach for the early diagnosis of Alzheimer's disease

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dc.contributor Universitat de Vic. Escola Politècnica Superior
dc.contributor.author Gallego Jutglà, Esteve
dc.contributor.author Solé-Casals, Jordi
dc.contributor.author Vialatte, François B.
dc.contributor.author Elgendi, Mohamed
dc.contributor.author Cichocki, Andrej
dc.contributor.author Dauwels, Justin
dc.date.accessioned 2015-02-16T13:08:26Z
dc.date.available 2016-01-02T00:03:52Z
dc.date.created 2015
dc.date.issued 2015
dc.identifier.citation Gallego-Jutglà, E., Solé-Casals, J., Vialatte, F. -., Elgendi, M., Cichocki, A., & Dauwels, J. (2015). A hybrid feature selection approach for the early diagnosis of alzheimer's disease. Journal of Neural Engineering, 12(1) ca_ES
dc.identifier.issn 1741-2560
dc.identifier.issn 1741-2552
dc.identifier.uri http://hdl.handle.net/10854/3893
dc.description.abstract Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease from EEG. However, choosing suitable measures is a challenging task. Among other measures, frequency Relative Power and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency Relative Power on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate, looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing MCI and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4± 11.5). Main Results. Using a single feature to compute classification rates we achieve a performance of 78.33% for the MCI data set and of 97.56 % for Mild AD. Results are clearly improved using the multiple feature classification, where a classification rate of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using 4 features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results. en
dc.format application/pdf
dc.format.extent 37 p. ca_ES
dc.language.iso eng ca_ES
dc.publisher IOS Press ca_ES
dc.rights © IOP Publishing. The published version of the article is available at http://iopscience.iop.org/1741-2552/12/1/016018/article
dc.rights Tots els drets reservats ca_ES
dc.subject.other Alzheimer, Malaltia d' ca_ES
dc.title A hybrid feature selection approach for the early diagnosis of Alzheimer's disease en
dc.type info:eu-repo/semantics/article ca_ES
dc.embargo.terms 12 mesos ca_ES
dc.identifier.doi https://doi.org/10.1088/1741-2560/12/1/016018
dc.rights.accessRights info:eu-repo/semantics/openAccess ca_ES
dc.titol.revista Indexat a WOS/JCR
dc.type.version info:eu-repo/acceptedVersion ca_ES
dc.indexacio Indexat a SCOPUS ca_ES
dc.indexacio Indexat a WOS/JCR

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