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dc.contributor.authorMcDermott, Barry
dc.contributor.authorDunne, Eoghan
dc.contributor.authorO’Halloran, Martin
dc.contributor.authorPorter, Emily
dc.contributor.authorSantorelli, Adam
dc.date.accessioned2019-09-13T07:45:27Z
dc.date.issued2019-08-28
dc.identifier.citationMcDermott B., Dunne E., O’Halloran M., Porter E., Santorelli A. (2019) Brain Haemorrhage Detection Through SVM Classification of Electrical Impedance Tomography Measurements. In: Makarov S., Horner M., Noetscher G. (eds) Brain and Human Body Modeling. Springer, Chamen_IE
dc.identifier.isbn978-3-030-21293-3
dc.identifier.urihttp://hdl.handle.net/10379/15431
dc.description.abstractA brain haemorrhage constitutes a serious medical scenario with a need for rapid, accurate detection to facilitate treatment initiation. Machine learning (ML) techniques applied to such medical diagnostic problems can improve the rate and accuracy of bleed detection leading to improved patient outcomes. In this chapter we examine the potential role of support vector machine (SVM) type classifiers in detecting such haemorrhagic lesions (bleeds) using electrical impedance tomography (EIT) measurement frames as the source of training and test data. A two-layer computational model of the head is designed, with EIT frame generation simulated from electrodes placed on the surface of the head model. A wide variety of test scenarios are modelled, including variations in measurement noise, bleed size and location, electrode position, and anatomy. Initial results using a linear SVM classifier applied to test scenarios, with and without pre-processing of the EIT measurement frame, are summarised. The classifier returned detection accuracies >90% with signal-to-noise ratios of ≥60 dB; was independent of bleed location, capable of detecting bleeds as small as 10 ml; and was unaffected by slight variances of ±2 mm in electrode position. However, the performance was degraded with anatomical variations. Options for improvement of performance, including selection of a different kernel and pre-processing of the frames prior to implementing the classifier, are then examined. This analysis demonstrated that using the radial basis function as the kernel for the SVM classifier and principal component analysis (PCA) to select specific features leads to the most accurate and robust performance. The analysis and results indicate that the coupling of EIT with ML has potential for improvement in the detection of bleeds such as brain haemorrhages.en_IE
dc.description.sponsorshipThe research leading to these results has received funding from the European Research Council under the European Union’s Horizon 2020 Programme/ERC Grant Agreement BioElecPro n.637780, Science Foundation Ireland (SFI) grant number 15/ERCS/3276, the Hardiman Research Scholarship from NUIG, the charity RESPECT, the Irish Research Council GOIPD/2017/854 fund, and the People Programme (Marie Curie Action) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA Grant Agreement no. PCOFUND-GA-2013-608728.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherSpringer, Chamen_IE
dc.relation.ispartofBrain and Human Body Modeling: Computational Human Modeling at EMBC 2018en
dc.subjectBrain haemorrhageen_IE
dc.subjectClassificationen_IE
dc.subjectElectrical impedance tomographyen_IE
dc.subjectSupport vector machinesen_IE
dc.titleBrain haemorrhage detection through SVM classification of electrical impedance tomography measurementsen_IE
dc.typeBook chapteren_IE
dc.date.updated2019-09-09T09:57:07Z
dc.identifier.doi10.1007/978-3-030-21293-3_12
dc.local.publishedsourcehttps://doi.org/10.1007/978-3-030-21293-3_12en_IE
dc.description.peer-reviewedPeer reviewed
dc.contributor.funderHorizon 2020en_IE
dc.contributor.funderEuropean Research Councilen_IE
dc.contributor.funderHardiman Research Scholarship, National University of Ireland Galwayen_IE
dc.contributor.funderRESPECTen_IE
dc.contributor.funderIrish Research Councilen_IE
dc.contributor.funderSeventh Framework Programmeen_IE
dc.description.embargo2021-08-28
dc.internal.rssid17588481
dc.local.contactBarry Mc Dermott, Translational Medical Device Lab, , 2nd Floor Lambe Translational Research Facility,, University College Hospital, , Galway. - Email: b.mcdermott3@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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