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Generalization assessment of non-invasive black-box temperature estimators from therapeutic ultrasound

2007Journal article Restricted access
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dc.contributor.authorTeixeira, C. A.
dc.contributor.authorRuano, Antonio
dc.contributor.authorRuano, M. Graça
dc.contributor.authorPereira, W. C. A.
dc.date.accessioned2013-02-08T14:18:01Z
dc.date.available2013-02-08T14:18:01Z
dc.date.issued2007
dc.date.updated2013-01-26T18:14:31Z
dc.description.abstractThe objective of this work is the generalisation performance assessment, in terms of intensity, of non-invasive temperature models based on radial basis functions neural networks. The models were built considering data collected at three therapeutic ultrasound intensities, (among 0.5, 1.0, 1.5 and 2.0 W/cm2) and then were validated in fresh data, which contain information from the trained intensities and form the untrained intensity. The models were built to estimate the temperature evolution (during 35 min) in a gel-based phantom, heated by physiotherapeutic ultrasound at four different intensities. It was found that the best models built without data from the intermediate intensities (0.5, 1.0 and 1.5 W/cm2) perform well in validation at all the intensities. On the other hand, the models built without data from the extrapolated intensity (2,0 W/cm2) presented unsatisfactory results in validation. This is because the models parameters were found considering a space bounded by the data used in their construction, and then the application of data outside this space resulted in poor performance. The models build without the intermediate data, for the three considered points, presented a maximum absolute error inferior to 0.5 ºC (which is accepted for therapeutic applications). The best models also presented a low computational complexity, as desired for real-time applications.por
dc.identifier.citationTeixeira, C. A.; Ruano, A. E.; Ruano, M. G.; Pereira, W.C . A. Generalization assessment of non-invasive black-box temperature estimators from therapeutic ultrasound, Revista Brasileira de Engenharia Biomédica, 23, 2, 143-151, 2007.por
dc.identifier.issn1517-3151
dc.identifier.otherAUT: ARU00698; MRU00118;
dc.identifier.urihttp://hdl.handle.net/10400.1/2262
dc.language.isoengpor
dc.peerreviewedyespor
dc.subjectNon-invasive temperature estimationpor
dc.subjectData-driven modelspor
dc.subjectRadial basis functions neural networkspor
dc.subjectMulti-objective genetic algorithmspor
dc.subjectUltrasoundpor
dc.subjectPhysiotherapypor
dc.titleGeneralization assessment of non-invasive black-box temperature estimators from therapeutic ultrasoundpor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage151por
oaire.citation.issue2por
oaire.citation.startPage143por
oaire.citation.titleRevista Brasileira de Engenharia Biomédicapor
oaire.citation.volume23por
person.familyNameTeixeira
person.familyNameRuano
person.familyNameRuano
person.familyNamePereira
person.givenNameCésar
person.givenNameAntonio
person.givenNameMaria
person.givenNameWagner
person.identifier.ciencia-id9811-A0DD-D5A5
person.identifier.orcid0000-0001-9396-1211
person.identifier.orcid0000-0002-6308-8666
person.identifier.orcid0000-0002-0014-9257
person.identifier.orcid0000-0001-5880-3242
person.identifier.ridA-3477-2012
person.identifier.ridB-4135-2008
person.identifier.ridA-8321-2011
person.identifier.scopus-author-id55826531700
person.identifier.scopus-author-id7004284159
person.identifier.scopus-author-id7004483805
person.identifier.scopus-author-id35581987400
rcaap.rightsrestrictedAccesspor
rcaap.typearticlepor
relation.isAuthorOfPublication29e9844d-9355-4f2a-badf-9e7ad3117cdb
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication61fc8492-d73f-46ca-a3a3-4cd762a784e6
relation.isAuthorOfPublication5f0824cf-c471-4f03-8134-8003affbabe3
relation.isAuthorOfPublication.latestForDiscovery5f0824cf-c471-4f03-8134-8003affbabe3

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