Using Nonlinear Features for Fetal Heart Rate Classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00193060" target="_blank" >RIV/68407700:21230/12:00193060 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/65269705:_____/12:#0001674 RIV/00064165:_____/12:13565

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S1746809411000619" target="_blank" >http://www.sciencedirect.com/science/article/pii/S1746809411000619</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.bspc.2011.06.008" target="_blank" >10.1016/j.bspc.2011.06.008</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Using Nonlinear Features for Fetal Heart Rate Classification

Popis výsledku v původním jazyce

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 thispaper 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 evalua

Název v anglickém jazyce

Using Nonlinear Features for Fetal Heart Rate Classification

Popis výsledku anglicky

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 thispaper 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 evalua

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

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

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Biomedical Signal Processing and Control

ISSN

1746-8094

e-ISSN

—

Svazek periodika

4

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

8

Strana od-do

350-357

Kód UT WoS článku

000304843400005

EID výsledku v databázi Scopus

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