A Novel Software for Comprehensive Analysis of Cardiotocography Signals CTG-OAS

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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.accessioned2024-08-04T20:56:20Z
dc.date.available2024-08-04T20:56:20Z
dc.date.issued2017
dc.departmentİnönü Üniversitesien_US
dc.description2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEYen_US
dc.description.abstractThe research interest in fetal heart rate (FHR) monitoring dates back to the 1960s, and the breakthrough on fetal surveillance has been seen during the 1990s with computerized systems. Notwithstanding the general use of cardiotocography (CTG) in fetal monitoring, the assessment of fetal well-being exhibits a significant inter-and even intra-observer variability. Computerized CTG analysis has seen as the most promising way to tackle of the main shortcomings of visual CTG assessment. In this study, a novel software developed for research purposes is introduced. The software named as CTG Open Access Software (CTG-OAS) characterizes FHR signals by using comprehensive features obtained from different fields such as morphological, linear, nonlinear, time-frequency, discrete wavelet transform, and image-based time-frequency domains. The software also covers the main procedures which are necessary for the context of machine learning. More specifically, CTG-OAS presents several tools for performing the preprocessing, feature extraction, feature selection, and classification processes. The proposed software was practiced on CTU-UHB database with 552 raw CTG samples. In addition, a case study with Support Vector Machine classifier was performed in the study via CTG-OAS. According to experimental results, statistical parameters were obtained as accuracy equal to 87.97%, sensitivity equal to 89.04%, specificity equal to 81.36% and, quality index equal to 85.11%.en_US
dc.description.sponsorshipIEEE Turkey Sect,Anatolian Scien_US
dc.identifier.isbn978-1-5386-1880-6
dc.identifier.urihttps://hdl.handle.net/11616/102220
dc.identifier.wosWOS:000426868700050en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2017 International Artificial Intelligence and Data Processing Symposium (Idap)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiomedical Signal Processingen_US
dc.subjectcardiotocographyen_US
dc.subjectfetal heart rateen_US
dc.subjectsoftwareen_US
dc.subjectmachine learningen_US
dc.subjectclassificationen_US
dc.titleA Novel Software for Comprehensive Analysis of Cardiotocography Signals CTG-OASen_US
dc.typeConference Objecten_US

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