Using Wavelet Transform for Cardiotocography Signals Classification
| dc.authorid | Cömert, Zafer/0000-0001-5256-7648 | |
| dc.authorid | Kocamaz, Adnan Fatih/0000-0002-7729-8322 | |
| dc.authorwosid | Cömert, Zafer/V-1446-2019 | |
| dc.authorwosid | Cömert, Zafer/F-1940-2016 | |
| dc.authorwosid | Kocamaz, Adnan Fatih/C-2820-2014 | |
| dc.contributor.author | Comert, Zafer | |
| dc.contributor.author | Kocamaz, Adnan Fatih | |
| dc.date.accessioned | 2024-08-04T20:43:56Z | |
| dc.date.available | 2024-08-04T20:43:56Z | |
| dc.date.issued | 2017 | |
| dc.department | İnönü Üniversitesi | en_US |
| dc.description | 25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEY | en_US |
| dc.description.abstract | As a fetal surveillance technique, cardiotocography (CTG) involves fetal heart rate (FHR), uterine contraction activities, and fetal movements. CTG is practiced as a primary diagnostic test throughout the world to identify events that may pose a risk to the fetus during pregnancy and delivery. In this work, FHR signals carrying vital information on fetus were analyzed by using Haar (haar), Daubechies (db5), and Symlets (sym5) mother wavelet families between levels 1 and 12. The traditionally used morphological and linear features are obtained from FHR. Also, p-norm, Frobenius form, infinity, and negative infinity norms which are obtained separately from the each of the wavelet components were used as a feature to support the classification. The obtained features were applied as an input to k nearest neighbors (kNN) and artificial neural network (ANN) classifiers in order to discriminate the normal and hypoxic fetuses. According to experimental results, 90.51% and 90.21% classification success on the discrimination of normal and hypoxic fetuses were achieved by using haar at level 4 and kNN. | en_US |
| dc.description.sponsorship | Turk Telekom,Arcelik A S,Aselsan,ARGENIT,HAVELSAN,NETAS,Adresgezgini,IEEE Turkey Sect,AVCR Informat Technologies,Cisco,i2i Syst,Integrated Syst & Syst Design,ENOVAS,FiGES Engn,MS Spektral,Istanbul Teknik Univ | en_US |
| dc.identifier.isbn | 978-1-5090-6494-6 | |
| dc.identifier.issn | 2165-0608 | |
| dc.identifier.scopus | 2-s2.0-85026307102 | en_US |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.uri | https://hdl.handle.net/11616/97908 | |
| dc.identifier.wos | WOS:000413813100016 | en_US |
| dc.identifier.wosquality | N/A | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | tr | en_US |
| dc.publisher | Ieee | en_US |
| dc.relation.ispartof | 2017 25th Signal Processing and Communications Applications Conference (Siu) | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Cardiotocography | en_US |
| dc.subject | fetal heart rate | en_US |
| dc.subject | wavelet transform | en_US |
| dc.subject | k-nearest neighbors | en_US |
| dc.subject | artificial neural network | en_US |
| dc.title | Using Wavelet Transform for Cardiotocography Signals Classification | en_US |
| dc.type | Conference Object | en_US |
