Prediction of preterm deliveries from EHG signals using machine learning.

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect...

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Autores principales:	Paul Fergus, Pauline Cheung, Abir Hussain, Dhiya Al-Jumeily, Chelsea Dobbins, Shamaila Iram
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a8e1c10e32bf47db993a5b9b70e4d8f0
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spelling	oai:doaj.org-article:a8e1c10e32bf47db993a5b9b70e4d8f02021-11-18T08:49:18ZPrediction of preterm deliveries from EHG signals using machine learning.1932-620310.1371/journal.pone.0077154https://doaj.org/article/a8e1c10e32bf47db993a5b9b70e4d8f02013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24204760/?tool=EBIhttps://doaj.org/toc/1932-6203There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier.Paul FergusPauline CheungAbir HussainDhiya Al-JumeilyChelsea DobbinsShamaila IramPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 10, p e77154 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Paul Fergus Pauline Cheung Abir Hussain Dhiya Al-Jumeily Chelsea Dobbins Shamaila Iram Prediction of preterm deliveries from EHG signals using machine learning.
description	There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier.
format	article
author	Paul Fergus Pauline Cheung Abir Hussain Dhiya Al-Jumeily Chelsea Dobbins Shamaila Iram
author_facet	Paul Fergus Pauline Cheung Abir Hussain Dhiya Al-Jumeily Chelsea Dobbins Shamaila Iram
author_sort	Paul Fergus
title	Prediction of preterm deliveries from EHG signals using machine learning.
title_short	Prediction of preterm deliveries from EHG signals using machine learning.
title_full	Prediction of preterm deliveries from EHG signals using machine learning.
title_fullStr	Prediction of preterm deliveries from EHG signals using machine learning.
title_full_unstemmed	Prediction of preterm deliveries from EHG signals using machine learning.
title_sort	prediction of preterm deliveries from ehg signals using machine learning.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/a8e1c10e32bf47db993a5b9b70e4d8f0
work_keys_str_mv	AT paulfergus predictionofpretermdeliveriesfromehgsignalsusingmachinelearning AT paulinecheung predictionofpretermdeliveriesfromehgsignalsusingmachinelearning AT abirhussain predictionofpretermdeliveriesfromehgsignalsusingmachinelearning AT dhiyaaljumeily predictionofpretermdeliveriesfromehgsignalsusingmachinelearning AT chelseadobbins predictionofpretermdeliveriesfromehgsignalsusingmachinelearning AT shamailairam predictionofpretermdeliveriesfromehgsignalsusingmachinelearning
_version_	1718421280012632064

Prediction of preterm deliveries from EHG signals using machine learning.

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