Performance Evaluation of Empirical Mode Decomposition and Discrete Wavelet Transform for Computerized Hypoxia Detection and Prediction

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dc.authoridKocamaz, Adnan Fatih/0000-0002-7729-8322
dc.authorwosidCömert, Zafer/F-1940-2016
dc.authorwosidKocamaz, Adnan Fatih/C-2820-2014
dc.contributor.authorComert, Zafer
dc.contributor.authorYang, Zhang
dc.contributor.authorVelappan, Subha
dc.contributor.authorBoopathi, A. Manivanna
dc.contributor.authorKocamaz, Adnan Fatih
dc.date.accessioned2024-08-04T20:45:21Z
dc.date.available2024-08-04T20:45:21Z
dc.date.issued2018
dc.departmentİnönü Üniversitesien_US
dc.description26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEYen_US
dc.description.abstractThis study proposes a new model relying on Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) in order to detect fetal hypoxia by using Cardiotocography (CTG) signals. We processed one well known open access intrapartum CTU-UHB database to find if our model could outperform the state-of-the art models. The model consists of three key stages: (1) Preprocessing, (2) Features extraction using EMD and DWT, (3) Classification with Support Vector Machine (SVM). Also, we present a comparative experimental study to measure the performance of SVM classifier depending on feature extraction methods. As a result, EMD and DWT have been found as useful methods for fetal hypoxia detection. Also, SVM classifier utilizing a combination of DWT and morphological features achieved the highest performance. Furthermore, DWT features produced more successful results than EMD features in terms of the classification success. Consequently, the proposed model ensured sensitivity of 57.42% and specificity of 70.11%.en_US
dc.description.sponsorshipIEEE,Huawei,Aselsan,NETAS,IEEE Turkey Sect,IEEE Signal Proc Soc,IEEE Commun Soc,ViSRATEK,Adresgezgini,Rohde & Schwarz,Integrated Syst & Syst Design,Atilim Univ,Havelsan,Izmir Katip Celebi Univen_US
dc.identifier.isbn978-1-5386-1501-0
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85050793016en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11616/98407
dc.identifier.wosWOS:000511448500096en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2018 26th Signal Processing and Communications Applications Conference (Siu)en_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.subjectempirical mode decompositionen_US
dc.subjectdiscrete wavelet transformen_US
dc.subjectsupport vector machineen_US
dc.titlePerformance Evaluation of Empirical Mode Decomposition and Discrete Wavelet Transform for Computerized Hypoxia Detection and Predictionen_US
dc.typeConference Objecten_US

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