Sparse Support Vector Machine for Intrapartum Fetal Heart Rate Classification

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Sparse Support Vector Machine for Intrapartum Fetal Heart Rate Classification

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2017

Confidentiality

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

Name of the periodical

IEEE Journal of Biomedical and Health Informatics

ISSN

2168-2194

e-ISSN

—

Volume of the periodical

21

Issue of the periodical within the volume

3

Country of publishing house

US - UNITED STATES

Number of pages

8

Pages from-to

664-671

UT code for WoS article

000401096700010

EID of the result in the Scopus database

2-s2.0-85019265771