Cardiotocography Analysis based on Segmentation-based Fractal Texture Decomposition and Extreme Learning Machine

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dc.authoridCömert, Zafer/0000-0001-5256-7648
dc.authoridKocamaz, Adnan Fatih/0000-0002-7729-8322
dc.authorwosidCömert, Zafer/V-1446-2019
dc.authorwosidKocamaz, Adnan Fatih/C-2820-2014
dc.contributor.authorComert, Zafer
dc.contributor.authorKocamaz, Adnan Fatih
dc.date.accessioned2024-08-04T20:43:56Z
dc.date.available2024-08-04T20:43:56Z
dc.date.issued2017
dc.departmentİnönü Üniversitesien_US
dc.description25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEYen_US
dc.description.abstractFetal heart rate (FHR) has notable patterns for the assessment of fetal physiology and typical stress conditions. FHR signals are obtained using cardiotocography (CTG) devices also providing uterine activities simultaneously and fetal movements. In this study, a total of 88 records consisting of 44 normal and 44 hypoxic fetuses instances obtained from publicly available CTU-UHB database have been considered. The basic morphological features supporting clinical diagnosis, the powers of 4 different spectral bands and Lempel Ziv complexity have been used to define FHR signals. Also, it has been proposed to use segmentation-based fractal texture analysis (SFTA) to identify the signals more accurately. The obtained feature set was applied as the input to extreme learning machine (ELM) with 5-fold cross-validation method. According to experimental results, 79.65% of accuracy, 79.92% of specificity, and 80.95% of sensitivity were obtained. It was observed that the SFTA offers useful statistical features to distinguish normal and hypoxic fetuses.en_US
dc.description.sponsorshipTurk Telekom,Arcelik A S,Aselsan,ARGENIT,HAVELSAN,NETAS,Adresgezgini,IEEE Turkey Sect,AVCR Informat Technologies,Cisco,i2i Syst,Integrated Syst & Syst Design,ENOVAS,FiGES Engn,MS Spektral,Istanbul Teknik Univen_US
dc.identifier.isbn978-1-5090-6494-6
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85026295993en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11616/97905
dc.identifier.wosWOS:000413813100260en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2017 25th Signal Processing and Communications Applications Conference (Siu)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCardiotocographyen_US
dc.subjectfetal heart rateen_US
dc.subjectsegmentation-based fractal texture analysisen_US
dc.subjectextreme learning machineen_US
dc.titleCardiotocography Analysis based on Segmentation-based Fractal Texture Decomposition and Extreme Learning Machineen_US
dc.typeConference Objecten_US

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