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dc.contributor.authorDunne, Eoghan
dc.contributor.authorSantorelli, Adam
dc.contributor.authorMcGinley, Brian
dc.contributor.authorO'Halloran, Martin
dc.contributor.authorPorter, Emily
dc.date.accessioned2018-12-12T15:26:32Z
dc.date.available2018-12-12T15:26:32Z
dc.date.issued2018-11-08
dc.identifier.citationDunne, E., Santorelli, A., McGinley, B., O’Halloran, M., & Porter, E. (2018, 10-13 Sept. 2018). Linear Regression for Estimating Bladder Volume with Voltage Signals. 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.urihttp://hdl.handle.net/10379/14694
dc.description.abstractUrinary incontinence is a common condition that can severely impact the lives of those who have it. Bladder volume monitoring solutions that exploit the electrical differences of different tissues in the pelvis have the potential to help medical personnel in the decision-making process with urinary incontinence. In this work, we investigate linear regression as a means of assigning bladder volume to the measured voltage values. We found that linear regression outperforms the previously studied machine learning regression algorithms by nearly a factor of 4. This linear regression approach is also more effectively able to handle volumes outside the training boundaries in comparison to previous work in the field. More work is needed to further improve the estimate of bladder volume based on the voltage signals, especially at high noise levels.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 and 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. This publication is based upon work from COST Action EMF-MED, supported by COST (European Cooperation in Science and Technology).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.subjectRegressionen_IE
dc.subjectVoltageen_IE
dc.subjectElectrical Impedanceen_IE
dc.subjectCOST EMF-MEDen_IE
dc.titleLinear regression for estimating bladder volume with voltage signalsen_IE
dc.typeConference Paperen_IE
dc.date.updated2018-12-10T14:19:55Z
dc.identifier.doi10.23919/EMF-MED.2018.8526019
dc.local.publishedsourcehttps://dx.doi.org/10.23919/EMF-MED.2018.8526015en_IE
dc.description.peer-reviewedpeer-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.internal.rssid14921633
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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