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dc.contributor.authorBhavsar, Ronakben
dc.contributor.authorDavey, Neil
dc.contributor.authorHelian, Na
dc.contributor.authorSun, Yi
dc.contributor.authorSteffert, Tony
dc.contributor.authorMayor, David
dc.date.accessioned2019-01-16T13:45:02Z
dc.date.available2019-01-16T13:45:02Z
dc.date.issued2018-12-11
dc.identifier.citationBhavsar , R , Davey , N , Helian , N , Sun , Y , Steffert , T & Mayor , D 2018 , ' Time Series Analysis using Embedding Dimension on Heart Rate Variability ' , Procedia Computer Science , vol. 145 , pp. 89-96 . https://doi.org/10.1016/j.procs.2018.11.015
dc.identifier.issn1877-0509
dc.identifier.otherPURE: 15912578
dc.identifier.otherPURE UUID: cad71a09-b98f-43d1-a094-77fb785c4485
dc.identifier.otherScopus: 85059471250
dc.identifier.urihttp://hdl.handle.net/2299/20971
dc.description.abstractHeart Rate Variability (HRV) is the measurement sequence with one or more visible variables of an underlying dynamic system, whose state changes with time. In practice, it is difficult to know what variables determine the actual dynamic system. In this research, Embedding Dimension (ED) is used to find out the nature of the underlying dynamical system. False Nearest Neighbour (FNN) method of estimating ED has been adapted for analysing and predicting variables responsible for HRV time series. It shows that the ED can provide the evidence of dynamic variables which contribute to the HRV time series. Also, the embedding of the HRV time series into a four-dimensional space produced the smallest number of FNN. This result strongly suggests that the Autonomic Nervous System that drives the heart is a two features dynamic system: sympathetic and parasympathetic nervous system.en
dc.format.extent8
dc.language.isoeng
dc.relation.ispartofProcedia Computer Science
dc.rightsOpen
dc.subjectEmbedding Dimension
dc.subjectFalse Nearest Neighbours
dc.subjectHRV
dc.subjectLinear Regression
dc.subjectParasympathetic
dc.subjectSympathetic
dc.subjectTime series analysis
dc.subjectComputer Science(all)
dc.titleTime Series Analysis using Embedding Dimension on Heart Rate Variabilityen
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85059471250&partnerID=8YFLogxK
dc.relation.schoolSchool of Computer Science
dc.description.versiontypeFinal Published version
dcterms.dateAccepted2018-12-11
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1016/j.procs.2018.11.015
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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