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dc.contributor.authorSantorelli, Adam
dc.contributor.authorDunne, Eoghan
dc.contributor.authorPorter, Emily
dc.contributor.authorO'Halloran, Martin
dc.date.accessioned2018-12-12T14:54:16Z
dc.date.available2018-12-12T14:54:16Z
dc.date.issued2018-11-08
dc.identifier.citationSantorelli, Adam, Dunne, Eoghan , Porter, Emily , & O'Halloran, Martin. (2018). Multiclass SVM for bladder volume monitoring using electrical impedance measurements. Paper presented at the EMF-MED 2018, 1st EMF-Med World Conference on Biomedical Applications of Electromagnetic Fields, Split, Croatia, 10-13 September.en_IE
dc.identifier.isbn10.23919/EMF-MED.2018.8526015
dc.identifier.urihttp://hdl.handle.net/10379/14693
dc.description.abstractUrinary incontinence is a common condition that impacts the quality of life from those who suffer from it. Electrical impedance measurements offer the potential for a non-invasive low-cost solution to monitor changes in the bladder volume. This work focuses on using a multiclass support vector machine (SVM) algorithm to classify the fullness of the bladder into three states; not full, full, and a boundary class. This paper applies this machine learning algorithm to both simulation and experimental data. The SVM model uses the recorded voltages from electrical impedance measurements as features, is trained and optimized using a Bayesian Optimization approach, and then 10-fold cross-tested to obtain a generalized error. This paper demonstrates that simulation data with a signal-to-noise ratio of 40 dB, and experimental data from a pelvis phantom, can be perfectly separated into the three classes defined above.en_IE
dc.description.sponsorshipThis research was supported by the European Research Council under the European Union’s Horizon 2020 Programme/ERC Grant Agreement BioElecPro n. 637780, the charity RESPECT and the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA Grant Agreement no. PCOFUND-GA-2013-608728, and the IRC GOIPD GOIPD/2017/854. This work was developed within the framework of the COST Action BM1309 EMF-MED.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherIEEEen_IE
dc.relation.ispartofEMF-MED 2018en
dc.subjectBladder volume monitoringen_IE
dc.subjectMachine learningen_IE
dc.subjectElectrical impedanceen_IE
dc.subjectClassification algorithmsen_IE
dc.titleMulticlass SVM for bladder volume monitoring using electrical impedance measurementsen_IE
dc.typeConference Paperen_IE
dc.date.updated2018-12-10T14:18:31Z
dc.local.publishedsourcehttps://dx.doi.org/10.23919/EMF-MED.2018.8526015en_IE
dc.description.peer-reviewednon-peer-reviewed
dc.contributor.funderEuropean Research Councilen_IE
dc.contributor.funderHorizon 2020en_IE
dc.contributor.funderRESPECTen_IE
dc.contributor.funderFP7 People: Marie-Curie Actionsen_IE
dc.contributor.funderIrish Research Councilen_IE
dc.internal.rssid14921635
dc.local.contactEoghan Dunne, -. - Email: e.dunne13@nuigalway.ie
dc.local.copyrightcheckedYes
dc.local.versionACCEPTED
dcterms.projectinfo:eu-repo/grantAgreement/EC/H2020::ERC::ERC-STG/637780/EU/Frontier Research on the Dielectric Properties of Biological Tissue/BIOELECPROen_IE
dcterms.projectinfo:eu-repo/grantAgreement/EC/FP7::SP3::PEOPLE/608728/EU/Assistive Technologies in Autism and Intellectual Disability/ASSISTIDen_IE
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