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dc.contributor.authorGharehbaghi, Arash
dc.contributor.authorBabic, Ankica
dc.date.accessioned2022-07-05T12:16:20Z
dc.date.available2022-07-05T12:16:20Z
dc.date.created2022-07-02T12:04:13Z
dc.date.issued2022
dc.identifier.issn0926-9630
dc.identifier.urihttps://hdl.handle.net/11250/3002761
dc.description.abstractThis paper explores the capabilities of a sophisticated deep learning method, named Deep Time Growing Neural Network (DTGNN), and compares its possibilities against a generally well-known method, Convolutional Neural network (CNN). The comparison is performed by using time series of the heart sound signal, so-called Phonocardiography (PCG). The classification objective is to discriminate between healthy and patients with cardiac diseases by applying a deep machine learning method to PCGs. This approach which is called intelligent phonocardiography has received interest from the researchers toward the development of a smart stethoscope for decentralized diagnosis of heart disease. It is found that DTGNN associates further flexibility to the approach which enables the classifier to learn subtle contents of PCG, and meanwhile better copes with the complexities intrinsically that exist in the medical applications such as the imbalance training. The structural risk of the two methods is compared using the A-Test method.en_US
dc.language.isoengen_US
dc.publisherIOS Pressen_US
dc.rightsNavngivelse-Ikkekommersiell 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/deed.no*
dc.titleDeep Time Growing Neural Network vs Convolutional Neural Network for Intelligent Phonocardiographyen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2022 The authors and IOS Pressen_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.3233/SHTI220772
dc.identifier.cristin2036832
dc.source.journalStudies in Health Technology and Informaticsen_US
dc.source.pagenumber491-494en_US
dc.identifier.citationStudies in Health Technology and Informatics. 2022, 491-494.en_US


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Navngivelse-Ikkekommersiell 4.0 Internasjonal
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