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Linear versus non-linear non-invasive temperature predictors in a homogeneous medium subjected to physiotherapeutic ultrasound

2006Journal article Restricted access
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dc.contributor.authorTeixeira, C. A.
dc.contributor.authorRuano, M. Graça
dc.contributor.authorPereira, W. C. A.
dc.contributor.authorRuano, Antonio
dc.contributor.authorNegreira, C.
dc.date.accessioned2013-02-06T15:36:59Z
dc.date.available2013-02-06T15:36:59Z
dc.date.issued2006
dc.date.updated2013-01-26T18:46:12Z
dc.description.abstractThe lack of accurate time-spatial temperature estimators/predictors conditions the safe application of thermal therapies, such as hyperthermia. In this paper, a comparison between a linear and a non-linear class of models for non-invasive temperature prediction in a homogeneous medium, subjected to ultrasound at physiotherapeutic levels is presented. The linear models used were autoregressive with exogenous inputs (ARX) and the non-linear models were radial basis functions neural networks (RBFNN). In order to create and validate the models, an experiment was build to extract in vitro ultrasound RF-lines, as well as its correspondent temperature values. Then, features were extracted from the measured RF-lines and the models were trained and validated. For both the models, the best-fitted structures were selected using the multi-objective genetic algorithm (MOGA), given the enormous number of possible structures. The best RBFNN model presented a maximum absolute predictive error in the validation set five times less than the value presented by the best ARX model. In this work, the best RBFNN reached a maximum absolute error of 0.42 ºC, which is bellow the value pointed as a borderline between an appropriate and an undesired temperature estimator, which is 0.5 ºC. The average error was one order of magnitude less in the RBFNN case, and a less biased estimation was met. In addition, the best RBFNN needed less environmental information (inputs), given the capacity to non-linearly relate the information. The results obtained are encouraging, considering that coherent results should be obtained in a time-spatial modelling schema using RBFNN models.por
dc.identifier.citationTeixeira, C. A.; Ruano, M. G.; Pereira, W. C. A.; Ruano, A. E.; Negreira, C. Linear versus non-linear non-invasive temperature predictors in a homogeneous medium subjected to physiotherapeutic ultrasound, Revista Brasileira de Engenharia Biomédica, 22, 2, 131-141, 2006.por
dc.identifier.issn1517-3151
dc.identifier.otherAUT: MRU00118; ARU00698;
dc.identifier.urihttp://hdl.handle.net/10400.1/2244
dc.language.isoengpor
dc.peerreviewedyespor
dc.subjectNon-invasive temperature estimationpor
dc.subjectPhysiotherapeutic ultrasoundpor
dc.subjectRadial basis functions neural networkspor
dc.subjectMulti-objective genetic algorithmspor
dc.titleLinear versus non-linear non-invasive temperature predictors in a homogeneous medium subjected to physiotherapeutic ultrasoundpor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage141por
oaire.citation.issue2por
oaire.citation.startPage131por
oaire.citation.titleRevista Brasileira de Engenharia Biomédicapor
oaire.citation.volume22por
person.familyNameTeixeira
person.familyNameRuano
person.familyNamePereira
person.familyNameRuano
person.givenNameCésar
person.givenNameMaria
person.givenNameWagner
person.givenNameAntonio
person.identifier.ciencia-id9811-A0DD-D5A5
person.identifier.orcid0000-0001-9396-1211
person.identifier.orcid0000-0002-0014-9257
person.identifier.orcid0000-0001-5880-3242
person.identifier.orcid0000-0002-6308-8666
person.identifier.ridA-3477-2012
person.identifier.ridA-8321-2011
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id55826531700
person.identifier.scopus-author-id7004483805
person.identifier.scopus-author-id35581987400
person.identifier.scopus-author-id7004284159
rcaap.rightsopenAccesspor
rcaap.typearticlepor
relation.isAuthorOfPublication29e9844d-9355-4f2a-badf-9e7ad3117cdb
relation.isAuthorOfPublication61fc8492-d73f-46ca-a3a3-4cd762a784e6
relation.isAuthorOfPublication5f0824cf-c471-4f03-8134-8003affbabe3
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication.latestForDiscovery61fc8492-d73f-46ca-a3a3-4cd762a784e6

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