About: Using Nonlinear Features for Fetal Heart Rate Classification

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://www.sciencedirect.com/science/article/pii/S1746809411000619
Description	Fetal heart rate (FHR) is used to evaluate fetal well-being and enables clinicians to detect ongoing hypoxia during delivery. Routine clinical evaluation of intrapartum FHR is based on macroscopic morphological features visible to the naked eye. In this paper we evaluated conventional features and compared them to the nonlinear ones in the task of intrapartum FHR classification. The experiments were performed using a database of 217 FHR records with objective annotations, i.e. pH measurement. We have proven that the addition of nonlinear features improves accuracy of classification. The best classification results were achieved using a combination of conventional and nonlinear features with sensitivity of 73.4%, specificity of 76.3%, and F-measure of 71.9%. The best selected nonlinear features were: Lempel Ziv complexity, Sample entropy, and fractal dimension estimated by Higuchi method. Since the results of automatic signal evaluation are easily reproducible, the process of FHR evaluation can become more objective and may enable clinicians to focus on additional non-cardiotocography parameters influencing the fetus during delivery. Fetal heart rate (FHR) is used to evaluate fetal well-being and enables clinicians to detect ongoing hypoxia during delivery. Routine clinical evaluation of intrapartum FHR is based on macroscopic morphological features visible to the naked eye. In this paper we evaluated conventional features and compared them to the nonlinear ones in the task of intrapartum FHR classification. The experiments were performed using a database of 217 FHR records with objective annotations, i.e. pH measurement. We have proven that the addition of nonlinear features improves accuracy of classification. The best classification results were achieved using a combination of conventional and nonlinear features with sensitivity of 73.4%, specificity of 76.3%, and F-measure of 71.9%. The best selected nonlinear features were: Lempel Ziv complexity, Sample entropy, and fractal dimension estimated by Higuchi method. Since the results of automatic signal evaluation are easily reproducible, the process of FHR evaluation can become more objective and may enable clinicians to focus on additional non-cardiotocography parameters influencing the fetus during delivery. (en)
Title	Using Nonlinear Features for Fetal Heart Rate Classification Using Nonlinear Features for Fetal Heart Rate Classification (en)
skos:prefLabel	Using Nonlinear Features for Fetal Heart Rate Classification Using Nonlinear Features for Fetal Heart Rate Classification (en)
skos:notation	RIV/68407700:21230/12:00193060!RIV13-MSM-21230___
http://linked.open...avai/riv/aktivita	I P Z
http://linked.open...avai/riv/aktivity	I, P(NT11124), Z(MSM6840770012)
http://linked.open...iv/cisloPeriodika	7
http://linked.open...vai/riv/dodaniDat	2013
http://linked.open...aciTvurceVysledku	Huptych, Michal Spilka, Jiří Chudáček, Václav Lhotská, Lenka
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	176526
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/12:00193060
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Fetal Heart Rate; Cardiotocography; Nonlinear Methods; Feature Selection Classification (en)
http://linked.open.../riv/klicoveSlovo	Cardiotocography Fetal Heart Rate Feature Selection Classification Nonlinear Methods
http://linked.open...odStatuVydavatele	NL - Nizozemsko
http://linked.open...ontrolniKodProRIV	[50157A4A87EB]
http://linked.open...i/riv/nazevZdroje	Biomedical Signal Processing and Control
http://linked.open...in/vavai/riv/obor	JC
http://linked.open...ichTvurcuVysledku	4 (xsd:int)
http://linked.open...cetTvurcuVysledku	8 (xsd:int)
http://linked.open...vavai/riv/projekt	Impact of Cardiotocography evaluation by means of artificial inteligence on perinatal care
http://linked.open...UplatneniVysledku	2012
http://linked.open...v/svazekPeriodika	4
http://linked.open...iv/tvurceVysledku	Chudáček, Václav Huptych, Michal Janků, P. Koucký, M. Lhotská, Lenka Spilka, Jiří Georgoulas, G. Stylios, CH.
http://linked.open...ain/vavai/riv/wos	000304843400005
http://linked.open...n/vavai/riv/zamer	Transdisciplinární výzkum v oblasti biomedicínského inženýrství II
issn	1746-8094
number of pages	8 (xsd:int)
http://bibframe.org/vocab/doi	10.1016/j.bspc.2011.06.008
http://localhost/t...ganizacniJednotka	21230

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