Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach

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dc.authoridKocamaz, Adnan Fatih/0000-0002-7729-8322
dc.authoridComert, Zafer/0000-0001-5256-7648
dc.authorwosidKocamaz, Adnan Fatih/C-2820-2014
dc.authorwosidComert, Zafer/F-1940-2016
dc.contributor.authorComert, Zafer
dc.contributor.authorKocamaz, Adnan Fatih
dc.date.accessioned2024-08-04T20:44:34Z
dc.date.available2024-08-04T20:44:34Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.description7th Computer Science On-Line Conference (CSOC) -- APR, 2018 -- ELECTR NETWORKen_US
dc.description.abstractElectronic fetal monitoring (EFM) device which is used to record Fetal Heart Rate (FHR) and Uterine Contraction (UC) signals simultaneously is one of the significant tools in terms of the present obstetric clinical applications. In clinical practice, EFM traces are routinely evaluated with visual inspection by observers. For this reason, such a subjective interpretation has been caused various conflicts among observers to arise. Although the existing of international guidelines for ensuring more consistent assessment, the automated FHR analysis has been adopted as the most promising solution. In this study, an innovative approach based on deep convolutional neural network (DCNN) is proposed to classify FHR signals as normal and abnormal. The proposed method composes of three stages. FHR signals are passed through a set of preprocessing procedures in order to ensure more meaningful input images, firstly. Then, a visual representation of time-frequency information, spectrograms are obtained with the help of the Short Time Fourier Transform (STFT). Finally, DCNN method is utilized to classify FHR signals. To this end, the colored spectrograms images are used to train the network. In order to evaluate the proposed model, we conducted extensive experiments on the open CTU-UHB database considering the area under the receiver operating characteristic curve and other several performance metrics derived from the confusion matrix. Consequently, we achieved encouraging results.en_US
dc.description.sponsorshipOpenPublish.eu s r oen_US
dc.identifier.doi10.1007/978-3-319-91186-1_25
dc.identifier.endpage248en_US
dc.identifier.isbn978-3-319-91186-1
dc.identifier.isbn978-3-319-91185-4
dc.identifier.issn2194-5357
dc.identifier.issn2194-5365
dc.identifier.scopus2-s2.0-85047937599en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage239en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-319-91186-1_25
dc.identifier.urihttps://hdl.handle.net/11616/98314
dc.identifier.volume763en_US
dc.identifier.wosWOS:000445094400025en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer International Publishing Agen_US
dc.relation.ispartofSoftware Engineering and Algorithms in Intelligent Systemsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiomedical signal processingen_US
dc.subjectFetal monitoringen_US
dc.subjectDeep convolutional neural networken_US
dc.subjectClassificationen_US
dc.titleFetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approachen_US
dc.typeConference Objecten_US

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