Least Squares Support Vector Machines for FHR Classification and Assessing the pH Based Categorization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F16%3A00307025" target="_blank" >RIV/68407700:21730/16:00307025 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21460/16:00307025

Výsledek na webu

<a href="http://80.link.springer.com.dialog.cvut.cz/chapter/10.1007/978-3-319-32703-7_234" target="_blank" >http://80.link.springer.com.dialog.cvut.cz/chapter/10.1007/978-3-319-32703-7_234</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-32703-7_233" target="_blank" >10.1007/978-3-319-32703-7_233</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Least Squares Support Vector Machines for FHR Classification and Assessing the pH Based Categorization

Popis výsledku v původním jazyce

Cardiotocography (CTG) is the major monitoring tool for fetal well-being surveillance during labor. It consists of two distinctive signals: the Fetal Heart Rate (FHR) and the Uterine Contractions signal. The CTG interpretation is classically performed by obstetricians with visual inspection for reassuring or ominous patterns, which are associated with fetus' condition. Deviations of the CTG and especially of the (FHR) from normality can be an indication of oxygen deprivation during the stressful labor process, which can lead to major neurological damage to the fetus or even death. This compromise is usually reflected at the pH level of newborn's blood. Therefore pH levels are usually used for the discrimination between healthy and compromised fetuses. In this work we present our preliminary results of the application of a machine learning approach, using least squares support vector machines, to FHR classification using the largest CTG open-access database so far.

Název v anglickém jazyce

Least Squares Support Vector Machines for FHR Classification and Assessing the pH Based Categorization

Popis výsledku anglicky

Cardiotocography (CTG) is the major monitoring tool for fetal well-being surveillance during labor. It consists of two distinctive signals: the Fetal Heart Rate (FHR) and the Uterine Contractions signal. The CTG interpretation is classically performed by obstetricians with visual inspection for reassuring or ominous patterns, which are associated with fetus' condition. Deviations of the CTG and especially of the (FHR) from normality can be an indication of oxygen deprivation during the stressful labor process, which can lead to major neurological damage to the fetus or even death. This compromise is usually reflected at the pH level of newborn's blood. Therefore pH levels are usually used for the discrimination between healthy and compromised fetuses. In this work we present our preliminary results of the application of a machine learning approach, using least squares support vector machines, to FHR classification using the largest CTG open-access database so far.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

<a href="/cs/project/GP14-28462P" target="_blank" >GP14-28462P: Statistické metody zpracování signálu KTG při porodu zasazené do kontextu klinických informací</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

XIV MEDITERRANEAN CONFERENCE ON MEDICAL AND BIOLOGICAL ENGINEERING AND COMPUTING 2016

ISBN

978-3-319-32701-3

ISSN

1680-0737

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1205-1209

Název nakladatele

Springer

Místo vydání

New York

Místo konání akce

Paphos

Datum konání akce

31. 3. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000376283000233