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dc.contributor.authorDunne, Eoghan
dc.contributor.authorSantorelli, Adam
dc.contributor.authorMcGinley, Brian
dc.contributor.authorLeader, Geraldine
dc.contributor.authorO'Halloran, Martin
dc.contributor.authorPorter, Emily
dc.identifier.citationDunne, Eoghan, Santorelli, Adam, McGinley, Brian, Leader, Geraldine, O’Halloran, Martin, & Porter, Emily. (2018). Image-based classification of bladder state using electrical impedance tomography. Physiological Measurement, 39(12), 124001. doi:10.1088/1361-6579/aae6eden_IE
dc.description.abstractObjective: In this study, we examine the potential of using machine learning classification to determine the bladder state ( not full , full ) with electrical impedance tomography (EIT) images of the pelvic region. Accurate classification of these states would enable urinary incontinence (UI) monitoring to alert the patient, before involuntary voiding occurs, in a low-cost and discrete manner. Approach: Using both numerical and experimental data, we form datasets that contain diverse observations with varying clinical parameters such as bladder volume, urine conductivity, and the reference used for time-difference imaging. We then classify the bladder state using both pixel-wise and feature extraction-based classification techniques. We employ principal component analysis, wavelets, and image segmentation to help create features. Main results: The performance was compared across several classifier algorithms. The minimum accuracy was 77.50%. The highest accuracy observed was 100%, and was found by combining principal component analysis and the Gaussian radial based function kernel support vector machine. This combination also offered the best trade-off between classification performance and the costs of training time and memory space. The biggest challenge in bladder state classification is classifying volumes near the separation volume of not full and full, in which choosing the most suitable classifier combination can minimize this error. Significance: We performed the first machine learning classification of bladder EIT images, achieving high classification accuracies with both numerical and experimental data. This work highlights the potential of using image-based machine learning with an EIT device to support bladder monitoring for those suffering from UI.en_IE
dc.description.sponsorshipThis research was supported by funding from 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. The research was also supported by the Irish Research Council under the fund GOIPD/2017/854.en_IE
dc.publisherIOP Publishingen_IE
dc.relation.ispartofPhysiological Measurementen
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.subjectelectrical impedance tomographyen_IE
dc.subjectimage classificationen_IE
dc.subjecturinary incontinenceen_IE
dc.subjectfeature extractionen_IE
dc.subjectmachine learningen_IE
dc.titleImage-based classification of bladder state using electrical impedance tomographyen_IE
dc.contributor.funderH2020 European Research Councilen_IE
dc.contributor.funderSeventh Framework Programmeen_IE
dc.contributor.funderFP7 People: Marie-Curie Actionsen_IE
dc.contributor.funderIrish Research Councilen_IE
dc.local.contactEoghan Dunne, -. - Email:
dcterms.projectinfo:eu-repo/grantAgreement/EC/H2020::ERC::ERC-STG/637780/EU/Frontier Research on the Dielectric Properties of Biological Tissue/BIOELECPROen_IE

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