Predicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signals

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dc.authorid0000-0002-8264-3899en_US
dc.authorid0000-0001-5256-7648en_US
dc.authorid0000-0003-1840-9958
dc.contributor.authorAlsaggaf, Wafaa
dc.contributor.authorCömert, Zafer
dc.contributor.authorNour, Majid
dc.contributor.authorPolat, Kemal
dc.contributor.authorBrdesee, Hani
dc.contributor.authorToğaçar, Mesut
dc.date.accessioned2021-06-23T19:53:55Z
dc.date.available2021-06-23T19:53:55Z
dc.date.issued2020
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractCardiotocography (CTG) is a screening tool used in daily obstetric practice to determine fetal wellbeing. Its interpretation is generally performed visually by the field experts, and this visual inspection is an error-prone and subjective process. In addition, it leads to several drawbacks, such as variability among the observers and low reproducibility rates. To tackle these drawbacks, a novel computer-aided diagnostic (CAD) model is proposed. As novel diagnostic indices, the features provided by the common spatial patterns (CSP) were considered in this study. The experiments were carried out on a publicly available CTU-UHB Intrapartum CTG database. Four different data division criteria were evaluated individually. The proposed model relied upon a combination of the conventional as well as the CSP features and machine learning models such as an artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbor (kNN). To validate the successes of the models, the five-fold cross-validation method was employed. The results validated that the CSP features ensured an increase in the performances of the machine learning models in the fetal hypoxia detection task. Also, the most effective results were provided by the SVM classifier with an accuracy of 94.75%, a sensitivity of 74.29% and a specificity of 99.55%. Consequently, thanks to the proposed model, a novel, consistent, and robust diagnostic model ensured for predicting fetal hypoxia. (C) 2020 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.apacoust.2020.107429
dc.identifier.issn0003-682X
dc.identifier.issn1872-910X
dc.identifier.scopus2-s2.0-85085271155en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.apacoust.2020.107429
dc.identifier.urihttps://hdl.handle.net/20.500.12491/10329
dc.identifier.volume167en_US
dc.identifier.wosWOS:000539409800028en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofApplied Acousticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiomedical Signal Processingen_US
dc.subjectDecision Support Systemsen_US
dc.subjectCardiotocographyen_US
dc.subjectCommon Spatial Patternen_US
dc.subjectClassificationen_US
dc.titlePredicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signalsen_US
dc.typeArticleen_US

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