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dc.contributor.authorCapecci, E.en_NZ
dc.contributor.authorGholami Doborjeh, Zen_NZ
dc.contributor.authorMammone, Nen_NZ
dc.contributor.authorForesta, Fen_NZ
dc.contributor.authorMorabito, Fen_NZ
dc.contributor.authorKasabov, Nen_NZ
dc.date.accessioned2016-08-22T03:23:48Z
dc.date.available2016-08-22T03:23:48Z
dc.date.copyright2016-07-24en_NZ
dc.identifier.citationIEEE World Congress on Computational Intelligence 2016, Vancouver, Canada, 2016-07-24 to 2016-07-29en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/10000
dc.description.abstractMotivated by the dramatic rise of neurological disorders, we propose a SNN technique to model electroencephalography (EEG) data collected from people affected by Alzheimer’s Disease (AD) and people diagnosed with mild cognitive impairment (MCI). An evolving spatio-temporal data machine (eSTDM), named the NeuCube architecture, is used to analyse changes of neural activity across different brain regions. The model developed allows for studying AD progression and for predicting whether a patient diagnosed with MCI is more likely to develop AD.en_NZ
dc.publisherIEEE
dc.relation.urihttp://wcci2016.org/document/wcci16-pbk-c.pdf
dc.rightsCopyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.titleLongitudinal Study of Alzheimer’s Disease Degeneration through EEG Data Analysis with aNeuCube Spiking Neural Network Modelen_NZ
dc.typeConference Contribution
dc.rights.accessrightsOpenAccessen_NZ
aut.publication.placeIJCNN conference 2016 in Vancouver, Candaen_NZ
pubs.elements-id202511


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