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Classification of Physiological Sensor Signals Using Artificial Neural Networks

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dc.contributor.authorSharma, Nandita
dc.contributor.authorGedeon, Tamas (Tom)
dc.date.accessioned2015-12-10T23:22:11Z
dc.date.issued2013
dc.date.updated2015-12-10T10:28:57Z
dc.description.abstractPhysiological signals have certain prominent characteristics that distinguish them from other types of physiological signals which are familiar to experts and assessed by inspection. The aim of this paper is to develop a computational model that can disti
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/1885/66427
dc.publisherSpringer
dc.sourceLecture Notes in Computer Science (LNCS)
dc.titleClassification of Physiological Sensor Signals Using Artificial Neural Networks
dc.typeJournal article
local.bibliographicCitation.issue8227
local.bibliographicCitation.lastpage511
local.bibliographicCitation.startpage504
local.contributor.affiliationSharma, Nandita, College of Engineering and Computer Science, ANU
local.contributor.affiliationGedeon, Tamas (Tom), College of Engineering and Computer Science, ANU
local.contributor.authoruidSharma, Nandita, u4306724
local.contributor.authoruidGedeon, Tamas (Tom), u4088783
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor080602 - Computer-Human Interaction
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.ariespublicationu4334215xPUB1281
local.identifier.citationvolumeICONIP 2013, Part II
local.identifier.doi10.1007/978-3-642-42042-9_63
local.identifier.scopusID2-s2.0-84893360693
local.type.statusPublished Version

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