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dc.contributor.authorKasabov, N
dc.contributor.authorFeigin, V
dc.contributor.authorHou, Z-G
dc.contributor.authorChen, Y
dc.contributor.authorLiang, L
dc.contributor.authorKrishnamurthi, R
dc.contributor.authorOthman, M
dc.contributor.authorParmar, P
dc.date.accessioned2014-04-10T03:11:48Z
dc.date.available2014-04-10T03:11:48Z
dc.date.copyright2014
dc.date.issued2014-04-10
dc.identifier.citationNeurocomputing, vol.134, pp.269 - 279 (11)
dc.identifier.urihttp://hdl.handle.net/10292/7070
dc.description.abstractThe paper presents a novel method and system for personalised (individualised) modelling of spatio/spectro-temporal data (SSTD) and prediction of events. A novel evolving spiking neural network reservoir system (eSNNr) is proposed for the purpose. The system consists of: spike-time encoding module of continuous value input information into spike trains; a recurrent 3D SNNr; eSNN as an evolving output classifier. Such system is generated for every new individual, using existing data of similar individuals. Subject to proper training and parameter optimisation, the system is capable of accurate spatio- temporal pattern recognition (STPR) and of early prediction of individual events. The method and the system are generic, applicable to various SSTD and classification and prediction problems. As a case study, the method is applied for early prediction of occurrence of stroke on an individual basis. Preliminary experiments demonstrated a significant improvement in accuracy and time of event prediction when using the proposed method when compared with standard machine learning methods, such as MLR, SVM, MLP. Future development and applications are discussed.
dc.publisherElsevier
dc.relation.urihttp://dx.doi.org/10.1016/j.neucom.2013.09.049
dc.rightsCopyright © 2014 Elsevier Ltd. All rights reserved. This is the author’s version of a work that was accepted for publication in (see Citation). Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The definitive version was published in (see Citation). The original publication is available at (see Publisher's Version).
dc.subjectPersonalised modelling
dc.subjectSpatio-temporal pattern recognition
dc.subjectSpiking neural networks
dc.subjectEvolving connectionist systems
dc.subjectStroke occurrence prediction
dc.titleEvolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke
dc.typeJournal Article
dc.rights.accessrightsOpenAccess
dc.identifier.doi10.1016/j.neucom.2013.09.049
aut.relation.endpage279
aut.relation.pages11
aut.relation.startpage269
aut.relation.volume134
pubs.elements-id158783


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