Sparse learning for Intrapartum fetal heart rate analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F18%3A00324317" target="_blank" >RIV/68407700:21730/18:00324317 - isvavai.cz</a>

Výsledek na webu

<a href="http://iopscience.iop.org/article/10.1088/2057-1976/aabc64" target="_blank" >http://iopscience.iop.org/article/10.1088/2057-1976/aabc64</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/2057-1976/aabc64" target="_blank" >10.1088/2057-1976/aabc64</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sparse learning for Intrapartum fetal heart rate analysis

Popis výsledku v původním jazyce

Fetal Heart Rate (FHR) monitoring is used during delivery for fetal well-being assessment. Classically based on the visual evaluation of FIGO criteria, FHR characterization remains a challenging task that continuously receives intensive research efforts. Intrapartum FHR analysis is further complicated by the two different stages of labor (dilation and active pushing). Research works aimed at devising automated acidosis prediction procedures are either based on designing new advanced signal processing analyses or on efficiently combining a large number of features proposed in the literature. Such multi-feature procedures either rely on a prior feature selection step or end up with decision rules involving long lists of features. This many-feature outcome rule does not permit to easily interpret the decision and is hence not well suited for clinical practice. Machine-learning-based decision-rule assessment is often impaired by the use of different, proprietary and small databases, preventing meaningful comparisons of results reported in the literature. Here, sparse learning is promoted as a way to perform jointly feature selection and acidosis prediction, hence producing an optimal decision rule based on as few features as possible. Making use of a set of 20 features (gathering 'FIGO-like' features, classical spectral features and recently proposed scale-free features), applied to two large-size (respectively sime1800 and sime500 subjects), well-documented databases, collected independently in French and Czech hospitals, the benefits of sparse learning are quantified in terms of: (i) accounting for class imbalance (few acidotic subjects), (ii) producing simple and interpretable decision rules, (iii) evidences for differences between the temporal dynamics of active pushing and dilation stages, and (iv) of validity/generalizability of decision rules learned on one database and applied to the other one.

Název v anglickém jazyce

Sparse learning for Intrapartum fetal heart rate analysis

Popis výsledku anglicky

Fetal Heart Rate (FHR) monitoring is used during delivery for fetal well-being assessment. Classically based on the visual evaluation of FIGO criteria, FHR characterization remains a challenging task that continuously receives intensive research efforts. Intrapartum FHR analysis is further complicated by the two different stages of labor (dilation and active pushing). Research works aimed at devising automated acidosis prediction procedures are either based on designing new advanced signal processing analyses or on efficiently combining a large number of features proposed in the literature. Such multi-feature procedures either rely on a prior feature selection step or end up with decision rules involving long lists of features. This many-feature outcome rule does not permit to easily interpret the decision and is hence not well suited for clinical practice. Machine-learning-based decision-rule assessment is often impaired by the use of different, proprietary and small databases, preventing meaningful comparisons of results reported in the literature. Here, sparse learning is promoted as a way to perform jointly feature selection and acidosis prediction, hence producing an optimal decision rule based on as few features as possible. Making use of a set of 20 features (gathering 'FIGO-like' features, classical spectral features and recently proposed scale-free features), applied to two large-size (respectively sime1800 and sime500 subjects), well-documented databases, collected independently in French and Czech hospitals, the benefits of sparse learning are quantified in terms of: (i) accounting for class imbalance (few acidotic subjects), (ii) producing simple and interpretable decision rules, (iii) evidences for differences between the temporal dynamics of active pushing and dilation stages, and (iv) of validity/generalizability of decision rules learned on one database and applied to the other one.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Biomedical Physics & Engineering Express

ISSN

2057-1976

e-ISSN

2057-1976

Svazek periodika

4

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

—

Kód UT WoS článku

000434520900004

EID výsledku v databázi Scopus

2-s2.0-85047266507