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Article: Real-time data-reusing adaptive learning of a radial basis function network for tracking evoked potentials

TitleReal-time data-reusing adaptive learning of a radial basis function network for tracking evoked potentials
Authors
KeywordsConvergence rate
Data-reusing algorithm
Evoked potential
Radial basis function network
Tracking ability
Issue Date2006
PublisherIEEE.
Citation
Ieee Transactions On Biomedical Engineering, 2006, v. 53 n. 2, p. 226-237 How to Cite?
AbstractTracking variations in both the latency and amplitude of evoked potential (EP) is important in quantifying properties of the nervous system. Adaptive filtering is a powerful tool for tracking such variations. In this paper, a data-reusing non-linear adaptive filtering method, based on a radial basis function network (RBFN), is implemented to estimate EP. The RBFN consists of an input layer of source nodes, a single hidden layer of non-linear processing units and an output layer of linear weights. It has built-in nonlinear activation functions that allow learning of function mappings. Moreover, it produces satisfactory estimates of signals against a background noise without a priori knowledge of the signal, provided that the signal and noise are independent. In clinical situations where EP responses change rapidly, the convergence rate of the algorithm becomes a critical factor. A carefully designed data-reusing RBFN can accelerate the convergence rate markedly and, thus, enhance its performance. Both theoretical analysis and simulation results support the unproved performance of our new algorithm. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/44755
ISSN
2015 Impact Factor: 2.468
2015 SCImago Journal Rankings: 1.201
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorQiu, Wen_HK
dc.contributor.authorChang, Cen_HK
dc.contributor.authorLiu, Wen_HK
dc.contributor.authorPoon, PWFen_HK
dc.contributor.authorHu, Yen_HK
dc.contributor.authorLam, FKen_HK
dc.contributor.authorHamernik, RPen_HK
dc.contributor.authorWei, Gen_HK
dc.contributor.authorChan, FHYen_HK
dc.date.accessioned2007-10-30T06:09:30Z-
dc.date.available2007-10-30T06:09:30Z-
dc.date.issued2006en_HK
dc.identifier.citationIeee Transactions On Biomedical Engineering, 2006, v. 53 n. 2, p. 226-237en_HK
dc.identifier.issn0018-9294en_HK
dc.identifier.urihttp://hdl.handle.net/10722/44755-
dc.description.abstractTracking variations in both the latency and amplitude of evoked potential (EP) is important in quantifying properties of the nervous system. Adaptive filtering is a powerful tool for tracking such variations. In this paper, a data-reusing non-linear adaptive filtering method, based on a radial basis function network (RBFN), is implemented to estimate EP. The RBFN consists of an input layer of source nodes, a single hidden layer of non-linear processing units and an output layer of linear weights. It has built-in nonlinear activation functions that allow learning of function mappings. Moreover, it produces satisfactory estimates of signals against a background noise without a priori knowledge of the signal, provided that the signal and noise are independent. In clinical situations where EP responses change rapidly, the convergence rate of the algorithm becomes a critical factor. A carefully designed data-reusing RBFN can accelerate the convergence rate markedly and, thus, enhance its performance. Both theoretical analysis and simulation results support the unproved performance of our new algorithm. © 2006 IEEE.en_HK
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dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Transactions on Biomedical Engineeringen_HK
dc.rights©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectConvergence rateen_HK
dc.subjectData-reusing algorithmen_HK
dc.subjectEvoked potentialen_HK
dc.subjectRadial basis function networken_HK
dc.subjectTracking abilityen_HK
dc.subject.meshAdolescenten_HK
dc.subject.meshAdulten_HK
dc.subject.meshAlgorithmsen_HK
dc.subject.meshArtificial Intelligenceen_HK
dc.subject.meshBrain - physiopathologyen_HK
dc.subject.meshComputer Simulationen_HK
dc.subject.meshComputer Systemsen_HK
dc.subject.meshDiagnosis, Computer-Assisted - methodsen_HK
dc.subject.meshElectroencephalography - methodsen_HK
dc.subject.meshEvoked Potentialsen_HK
dc.subject.meshHumansen_HK
dc.subject.meshModels, Neurologicalen_HK
dc.subject.meshReaction Timeen_HK
dc.subject.meshReproducibility of Resultsen_HK
dc.subject.meshSensitivity and Specificityen_HK
dc.titleReal-time data-reusing adaptive learning of a radial basis function network for tracking evoked potentialsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0018-9294&volume=53&issue=2&spage=226&epage=237&date=2006&atitle=Real-time+data-reusing+adaptive+learning+of+a+radial+basis+function+network+for+tracking+evoked+potentialsen_HK
dc.identifier.emailChang, C: cqchang@eee.hku.hken_HK
dc.identifier.emailHu, Y: yhud@hku.hken_HK
dc.identifier.authorityChang, C=rp00095en_HK
dc.identifier.authorityHu, Y=rp00432en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/TBME.2005.862540en_HK
dc.identifier.pmid16485751-
dc.identifier.scopuseid_2-s2.0-31544482783en_HK
dc.identifier.hkuros117016-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-31544482783&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume53en_HK
dc.identifier.issue2en_HK
dc.identifier.spage226en_HK
dc.identifier.epage237en_HK
dc.identifier.isiWOS:000234882300009-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridQiu, W=36461603400en_HK
dc.identifier.scopusauthoridChang, C=7407033052en_HK
dc.identifier.scopusauthoridLiu, W=7407341280en_HK
dc.identifier.scopusauthoridPoon, PWF=24322414600en_HK
dc.identifier.scopusauthoridHu, Y=7407116091en_HK
dc.identifier.scopusauthoridLam, FK=7102075939en_HK
dc.identifier.scopusauthoridHamernik, RP=7005178310en_HK
dc.identifier.scopusauthoridWei, G=35499953100en_HK
dc.identifier.scopusauthoridChan, FHY=7202586429en_HK
dc.identifier.citeulike4698596-

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