The influences of different window functions and lengths on image-based time-frequency features of fetal heart rate signals

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dc.authorscopusid36543652400
dc.authorscopusid56705417300
dc.authorscopusid57203173287
dc.authorscopusid57203174317
dc.authorscopusid54882441600
dc.contributor.authorComert Z.
dc.contributor.authorBoopathi A.M.
dc.contributor.authorVelappan S.
dc.contributor.authorYang Z.
dc.contributor.authorKocamaz A.F.
dc.date.accessioned2024-08-04T20:04:01Z
dc.date.available2024-08-04T20:04:01Z
dc.date.issued2018
dc.departmentİnönü Üniversitesien_US
dc.descriptionAselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netasen_US
dc.description26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- 137780en_US
dc.description.abstractIn the clinical practice, the fetal distress conditions such as hypoxia are detected routinely during antepartum and even intrapartum periods with the help of electronic fetal monitoring device, often called Cardiotocography (CTG). Due to the noticeable advances in signal processing, pattern recognition, machine learning techniques and the introduction of the quantitative diagnostic indices, the automated CTG analysis has become a quite essential tool. In this study, we come up with a new investigation on the influences of different window functions on image-based time-frequency (IBTF) features of fetal heart rate (FHR) signals for fetal hypoxia detection. In addition to the traditionally used morphological features, the spectrogram images provided by Short Time Fourier Transform (STFT) were taken into account with different windows functions such as Hamming, Hann, Kaiser, and Blackman as well as different windows lengths. Then, the spectrogram images were converted into 8-bits grayscale images and IBTF features were obtained using Gray Level Co-occurrence Matrix (GLCM). At the end of the feature extraction stage for signal representation, we achieved a quite large feature set, and we employed genetic algorithm (GA) and support vector machine (SVM) classifier in order to reveal the most relevant features. The whole experiments were performed on an open CTU-UHB intrapartum CTG database. The experimental results show that the IBTF features have relatively increased the classification performance. All window functions ensured encouraging results. Furthermore, the GA ensured the determination of the 7 most relevant features. Thus, the dimension of feature space was reduced from 28 to 7. Moreover, the classification success increased. Consequently, the most efficient performances (Quality Index = 73.45%) were achieved with Hamming and Kaiser window functions. © 2018 IEEE.en_US
dc.identifier.doi10.1109/SIU.2018.8404247
dc.identifier.endpage4en_US
dc.identifier.isbn9781538615010
dc.identifier.scopus2-s2.0-85050799839en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1109/SIU.2018.8404247
dc.identifier.urihttps://hdl.handle.net/11616/92291
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof26th IEEE Signal Processing and Communications Applications Conference, SIU 2018en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiomedical signal processingen_US
dc.subjectClinical decision support systemen_US
dc.subjectFetal monitoringen_US
dc.subjectTexture featuresen_US
dc.titleThe influences of different window functions and lengths on image-based time-frequency features of fetal heart rate signalsen_US
dc.typeConference Objecten_US

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