Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy

Autor:	J. Mas-Cabo, G. Prats-Boluda, J. Garcia-Casado, J. Alberola-Rubio, R. Monfort-Ortiz, C. Martinez-Saez, A. Perales, Y. Ye-Lin
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	electrohysterogram uterine myoelectrical activity imminent labor prediction artificial network tocolytic therapy Chemical technology TP1-1185
Zdroj:	Sensors, Vol 20, Iss 9, p 2681 (2020)
Druh dokumentu:	article
ISSN:	1424-8220
DOI:	10.3390/s20092681
Popis:	Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy and entails high costs for health systems. Currently, no reliable labor proximity prediction techniques are available for clinical use. Regular checks by uterine electrohysterogram (EHG) for predicting preterm labor have been widely studied. The aim of the present study was to assess the feasibility of predicting labor with a 7- and 14-day time horizon in TPL women, who may be under tocolytic treatment, using EHG and/or obstetric data. Based on 140 EHG recordings, artificial neural networks were used to develop prediction models. Non-linear EHG parameters were found to be more reliable than linear for differentiating labor in under and over 7/14 days. Using EHG and obstetric data, the
Databáze:	Directory of Open Access Journals
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