Intrapartum Fetal Heart Rate Classification: Cross-Database Evaluation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F16%3A00305501" target="_blank" >RIV/68407700:21730/16:00305501 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-32703-7_231" target="_blank" >http://dx.doi.org/10.1007/978-3-319-32703-7_231</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-32703-7_231" target="_blank" >10.1007/978-3-319-32703-7_231</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Intrapartum Fetal Heart Rate Classification: Cross-Database Evaluation

Popis výsledku v původním jazyce

Fetal Heart Rate (FHR) provides obstetricians with essential information about fetal reactions to stress events during delivery. Early detection of fetal acidosis, enabling timely interventions and prevention of adverse consequences of acidosis for fetuses, remains a challenging task. In particular, the use of different, proprietary and small databases in various published works hinders meaningful comparisons of achieved results. This work relies on the the use of two independent databases in order to asses relevantly acidosis detection performance and to address important issues of knowledge transfer (features, classification model) from one database to the other. Using a large set of features, supervised classification is performed with state-of-the-art sparse support vector machines. It shows that selected features and classification performance are consistent for both databases. Further it quantifies the level of generalization of the achieved results, by making use of one database for learning and the other one for testing.

Název v anglickém jazyce

Intrapartum Fetal Heart Rate Classification: Cross-Database Evaluation

Popis výsledku anglicky

Fetal Heart Rate (FHR) provides obstetricians with essential information about fetal reactions to stress events during delivery. Early detection of fetal acidosis, enabling timely interventions and prevention of adverse consequences of acidosis for fetuses, remains a challenging task. In particular, the use of different, proprietary and small databases in various published works hinders meaningful comparisons of achieved results. This work relies on the the use of two independent databases in order to asses relevantly acidosis detection performance and to address important issues of knowledge transfer (features, classification model) from one database to the other. Using a large set of features, supervised classification is performed with state-of-the-art sparse support vector machines. It shows that selected features and classification performance are consistent for both databases. Further it quantifies the level of generalization of the achieved results, by making use of one database for learning and the other one for testing.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

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

6

Strana od-do

1193-1198

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

000376283000231