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dc.contributor.authorGharehbaghi, Arash
dc.contributor.authorSepehri, Amir A
dc.contributor.authorBabic, Ankica
dc.description.abstractThis paper presents an original machine learning method for extracting diagnostic medical information from heart sound recordings. The method is proposed to be integrated with an intelligent phonocardiography in order to enhance diagnostic value of this technology. The method is tailored to diagnose children with heart septal defects, the pathological condition which can bring irreversible and sometimes fatal consequences to the children. The study includes 115 children referrals to an university hospital, consisting of 6 groups of the individuals: atrial septal defects (10), healthy children with innocent murmur (25), healthy children without any murmur (25), mitral regurgitation (15), tricuspid regurgitation (15), and ventricular septal defect (25). The method is trained to detect the atrial or ventricular septal defects versus the rest of the groups. Accuracy/sensitivity and the structural risk of the method is estimated to be 91.6%/88.4% and 9.89%, using the repeated random sub sampling and the A-Test method, respectively.en_US
dc.publisherIOS Pressen_US
dc.rightsNavngivelse-Ikkekommersiell 4.0 Internasjonal*
dc.titleDistinguishing Septal Heart Defects from the Valvular Regurgitation Using Intelligent Phonocardiographyen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.rights.holderCopyright 2020 European Federation for Medical Informatics (EFMI) and IOS Pressen_US
dc.source.journalStudies in Health Technology and Informaticsen_US
dc.source.pagenumber178 - 182en_US
dc.identifier.citationStudies in Health Technology and Informatics. 2020, 270, 178 - 182.en_US

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Navngivelse-Ikkekommersiell 4.0 Internasjonal
Med mindre annet er angitt, så er denne innførselen lisensiert som Navngivelse-Ikkekommersiell 4.0 Internasjonal