Sparse Support Vector Machine for Intrapartum Fetal Heart Rate Classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F17%3A00311953" target="_blank" >RIV/68407700:21730/17:00311953 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/7440787/?reload=true&arnumber=7440787" target="_blank" >http://ieeexplore.ieee.org/document/7440787/?reload=true&arnumber=7440787</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/JBHI.2016.2546312" target="_blank" >10.1109/JBHI.2016.2546312</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sparse Support Vector Machine for Intrapartum Fetal Heart Rate Classification

Popis výsledku v původním jazyce

Fetal heart rate (FHR) monitoring is routinely used in clinical practice to help obstetricians assess fetal health status during delivery. However, early detection of fetal acidosis that allows relevant decisions for operative delivery remains a challenging task, receiving considerable attention. This contribution promotes sparse support vector machine classification that permits to select a small number of relevant features and to achieve efficient fetal acidosis detection. A comprehensive set of features is used for FHR description, including enhanced and computerized clinical features, frequency domain, and scaling and multifractal features, all computed on a large (1288 subjects) and well-documented database. The individual performance obtained for each feature independently is discussed first. Then, it is shown that the automatic selection of a sparse subset of features achieves satisfactory classification performance (sensitivity 0.73 and specificity 0.75, outperforming clinical practice). The subset of selected features (average depth of decelerations MAD(dtrd), baseline level beta(0), and variability H) receives simple interpretation in clinical practice. Intrapartum fetal acidosis detection is improved in several respects: A comprehensive set of features combining clinical, spectral, and scale-free dynamics is used; an original multivariate classification targeting both sparse feature selection and high performance is devised; state-ofthe-art performance is obtained on a much larger database than that generally studied with description of common pitfalls in supervised classification performance assessments.

Název v anglickém jazyce

Sparse Support Vector Machine for Intrapartum Fetal Heart Rate Classification

Popis výsledku anglicky

Fetal heart rate (FHR) monitoring is routinely used in clinical practice to help obstetricians assess fetal health status during delivery. However, early detection of fetal acidosis that allows relevant decisions for operative delivery remains a challenging task, receiving considerable attention. This contribution promotes sparse support vector machine classification that permits to select a small number of relevant features and to achieve efficient fetal acidosis detection. A comprehensive set of features is used for FHR description, including enhanced and computerized clinical features, frequency domain, and scaling and multifractal features, all computed on a large (1288 subjects) and well-documented database. The individual performance obtained for each feature independently is discussed first. Then, it is shown that the automatic selection of a sparse subset of features achieves satisfactory classification performance (sensitivity 0.73 and specificity 0.75, outperforming clinical practice). The subset of selected features (average depth of decelerations MAD(dtrd), baseline level beta(0), and variability H) receives simple interpretation in clinical practice. Intrapartum fetal acidosis detection is improved in several respects: A comprehensive set of features combining clinical, spectral, and scale-free dynamics is used; an original multivariate classification targeting both sparse feature selection and high performance is devised; state-ofthe-art performance is obtained on a much larger database than that generally studied with description of common pitfalls in supervised classification performance assessments.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

IEEE Journal of Biomedical and Health Informatics

ISSN

2168-2194

e-ISSN

—

Svazek periodika

21

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

664-671

Kód UT WoS článku

000401096700010

EID výsledku v databázi Scopus

2-s2.0-85019265771