Intrapartum fetal heart rate classification from trajectory in Sparse SVM feature space

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F15%3A00239724" target="_blank" >RIV/68407700:21730/15:00239724 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7318861&newsearch=true&queryText=%20Intrapartum%20fetal%20heart%20rate%20classification%20from%20trajectory%20in%20Sparse%20SVM%20feature%20spac" target="_blank" >http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7318861&newsearch=true&queryText=%20Intrapartum%20fetal%20heart%20rate%20classification%20from%20trajectory%20in%20Sparse%20SVM%20feature%20spac</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EMBC.2015.7318861" target="_blank" >10.1109/EMBC.2015.7318861</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Intrapartum fetal heart rate classification from trajectory in Sparse SVM feature space

Popis výsledku v původním jazyce

Intrapartum fetal heart rate (FHR) constitutes a prominent source of information for the assessment of fetal reactions to stress events during delivery. Yet, early detection of fetal acidosis remains a challenging signal processing task. The originalityof the present contribution are three-fold: multiscale representations and wavelet leader based multifractal analysis are used to quantify FHR variability ; Supervised classification is achieved by means of Sparse-SVM that aim jointly to achieve optimaldetection performance and to select relevant features in a multivariate setting ; Trajectories in the feature space accounting for the evolution along time of features while labor progresses are involved in the construction of indices quantifying fetal health. The classification performance permitted by this combination of tools are quantified on a intrapartum FHR large database (~ 1250 subjects) collected at a French academic public hospital.

Název v anglickém jazyce

Intrapartum fetal heart rate classification from trajectory in Sparse SVM feature space

Popis výsledku anglicky

Intrapartum fetal heart rate (FHR) constitutes a prominent source of information for the assessment of fetal reactions to stress events during delivery. Yet, early detection of fetal acidosis remains a challenging signal processing task. The originalityof the present contribution are three-fold: multiscale representations and wavelet leader based multifractal analysis are used to quantify FHR variability ; Supervised classification is achieved by means of Sparse-SVM that aim jointly to achieve optimaldetection performance and to select relevant features in a multivariate setting ; Trajectories in the feature space accounting for the evolution along time of features while labor progresses are involved in the construction of indices quantifying fetal health. The classification performance permitted by this combination of tools are quantified on a intrapartum FHR large database (~ 1250 subjects) collected at a French academic public hospital.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

<a href="/cs/project/NT11124" target="_blank" >NT11124: Vliv hodnocení kardiotokografie pomocí metod umělé inteligence na kvalitu perinatální péče</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

IEEE EMBC 2015 Proceedings (Milano)

ISBN

9781424492718

ISSN

1557-170X

e-ISSN

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Počet stran výsledku

4

Strana od-do

2335-2338

Název nakladatele

IEEE

Místo vydání

Milano

Místo konání akce

Milano

Datum konání akce

25. 8. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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