Classification and Comparison of Cardiotocography Signals with Artificial Neural Network and Extreme Learning Machine

Basit Öğe Kaydı
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.contributor.authorGungor, Sami
dc.date.accessioned2024-08-04T20:42:35Z
dc.date.available2024-08-04T20:42:35Z
dc.date.issued2016
dc.departmentİnönü Üniversitesien_US
dc.description24th Signal Processing and Communication Application Conference (SIU) -- MAY 16-19, 2016 -- Zonguldak, TURKEYen_US
dc.description.abstractCardiotocography (CTG) is a monitoring technique that is used routinely during pregnancy and labor to assess fetal well-being. CTG consists of two signals which are fetal heart rate (FHR) and uterine contraction (UC). Twenty-one features representing the characteristic of FHR have been used in this work. The features are obtained from a large dataset consisting of 2126 records in UCI Machine Learning Repository. The prominent features, such as baseline, the number of acceleration and deceleration patterns, and variability recommended by International Federation of Gynecology and Obstetrics (FIGO) have also taken into account during CTG analysis. The features were applied as the input to feedforward neural network (ANN) and Extreme Learning Machine (ELM) to classify FHR patterns in this study. FHR is recently divided into three classes as normal, suspicious and pathological. According to the results of this study, the accuracy of classification of ANN and ELM were obtained as 91.84% and 93.42%, respectively.en_US
dc.description.sponsorshipIEEE,Bulent Ecevit Univ, Dept Elect & Elect Engn,Bulent Ecevit Univ, Dept Biomed Engn,Bulent Ecevit Univ, Dept Comp Engnen_US
dc.identifier.endpage1496en_US
dc.identifier.isbn978-1-5090-1679-2
dc.identifier.scopus2-s2.0-84982803300en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1493en_US
dc.identifier.urihttps://hdl.handle.net/11616/97468
dc.identifier.wosWOS:000391250900350en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2016 24th Signal Processing and Communication Application 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 rate classificationen_US
dc.subjectfeedforward neural networken_US
dc.subjectextreme learning machineen_US
dc.titleClassification and Comparison of Cardiotocography Signals with Artificial Neural Network and Extreme Learning Machineen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu