Assessment of features for automatic CTG analysis based on expert Annotation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F65269705%3A_____%2F11%3A%230001549" target="_blank" >RIV/65269705:_____/11:#0001549 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/11:00203843 RIV/68407700:21230/11:00310219

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6091495" target="_blank" >http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6091495</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IEMBS.2011.6091495" target="_blank" >10.1109/IEMBS.2011.6091495</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Assessment of features for automatic CTG analysis based on expert Annotation

Popis výsledku v původním jazyce

Cardiotocography (CTG) is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO) since 1960's used routinely by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the ever-used features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and the features are assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. Annotation derived from the panel of experts instead of the commonly utilized pH values was used for evaluation of the features on a large data set (552 records). We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ra

Název v anglickém jazyce

Assessment of features for automatic CTG analysis based on expert Annotation

Popis výsledku anglicky

Cardiotocography (CTG) is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO) since 1960's used routinely by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the ever-used features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and the features are assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. Annotation derived from the panel of experts instead of the commonly utilized pH values was used for evaluation of the features on a large data set (552 records). We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ra

Druh

D - Stať ve sborníku

CEP obor

FK - Gynekologie a porodnictví

OECD FORD obor

—

Projekt

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

Návaznosti

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

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název statě ve sborníku

33rd Annual International Conference of the IEEE EMBS

ISBN

978-1-4244-4122-8

ISSN

1557-170X

e-ISSN

—

Počet stran výsledku

4

Strana od-do

6051-6054

Název nakladatele

IEEE

Místo vydání

Boston

Místo konání akce

Boston

Datum konání akce

1. 1. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000298810004242