Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment

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dc.authoridCömert, Zafer/0000-0001-5256-7648
dc.authoridVelappan, Subha/0000-0002-4992-4090
dc.authoridKocamaz, Adnan Fatih/0000-0002-7729-8322
dc.authorwosidCömert, Zafer/V-1446-2019
dc.authorwosidVelappan, Subha/AAF-1534-2020
dc.authorwosidPrince dr k Vasudevan college of engineering, Prince dr k Vasudevan college of engineering/JFB-2865-2023
dc.authorwosidKocamaz, Adnan Fatih/C-2820-2014
dc.contributor.authorComert, Zafer
dc.contributor.authorKocamaz, Adnan Fatih
dc.contributor.authorSubha, Velappan
dc.date.accessioned2024-08-04T20:44:34Z
dc.date.available2024-08-04T20:44:34Z
dc.date.issued2018
dc.departmentİnönü Üniversitesien_US
dc.description.abstractCardiotocography (CTG) is applied routinely for fetal monitoring during the perinatal period to decrease the rates of neonatal mortality and morbidity as well as unnecessary interventions. The analysis of CTG traces has become an indispensable part of present clinical practices; however, it also has serious drawbacks, such as poor specificity and variability in its interpretation. The automated CTG analysis is seen as the most promising way to overcome these disadvantages. in this study, a novel prognostic model is proposed for predicting fetal hypoxia from CTG traces based on an innovative approach called image-based time-frequency (IBTF) analysis comprised of a combination of short time Fourier transform (STFT) and gray level co-occurrence matrix (GLCM). More specifically, from a graphical representation of the fetal heart rate (FHR) signal, the spectrogram is obtained by using STFT. The spectrogram images are converted into 8-bit grayscale images, and IBTF features such as contrast, correlation, energy, and homogeneity are utilized for identifying FHR signals. At the final stage of the analysis, different subsets of the feature space are applied as the input to the least square support vector machine (LS-SVM) classifier to determine the most informative subset. For this particular purpose, the genetic algorithm is employed. The prognostic model was performed on the open-access intrapartum CTU-UHB CTG database. The sensitivity and specificity obtained using only conventional features were 57.33% and 67.24%, respectively, whereas the most effective results were achieved using a combination of conventional and IBTF features, with a sensitivity of 63.45% and a specificity of 65.88%. Conclusively, this study provides a new promising approach for feature extraction of FHR signals. In addition, the experimental outcomes showed that IBTF features provided an increase in the classification accuracy.en_US
dc.identifier.doi10.1016/j.compbiomed.2018.06.003
dc.identifier.endpage97en_US
dc.identifier.issn0010-4825
dc.identifier.issn1879-0534
dc.identifier.pmid29894897en_US
dc.identifier.scopus2-s2.0-85048152516en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage85en_US
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2018.06.003
dc.identifier.urihttps://hdl.handle.net/11616/98324
dc.identifier.volume99en_US
dc.identifier.wosWOS:000442978700008en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers in Biology and Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - 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.subjectGray level Co-Occurrence matrixen_US
dc.subjectImage -based time-frequency analysisen_US
dc.subjectClassificationen_US
dc.titlePrognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessmenten_US
dc.typeArticleen_US

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