Title: A machine learning system for automatic detection of preterm activity using artificial neural networks and uterine electromyography data

Authors: Paul Fergus; Abir Hussain; D. Al-Jumeily; Hani Hamdan

Addresses: Applied Computing Research Group, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK ' Applied Computing Research Group, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK ' Applied Computing Research Group, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK ' CentraleSupélec, Laboratoire des Signaux et Systèmes, UMR CNRS 8506, 3 rue Joliot Curie, 91190 Gif-sur-Yvette, France

Abstract: Preterm births are babies born before 37 weeks of gestation. The premature delivery of babies is a major global health issue with those affected at greater risk of developing short and long-term complications. Therefore, a better understanding of why preterm births occur is needed. Electromyography is used to capture electrical activity in the uterus to help treat and understand the condition, which is time consuming and expensive. This has led to a recent interest in automated detection of the electromyography correlates of preterm activity. This paper explores this idea further using artificial neural networks to classify term and preterm records, using an open dataset containing 300 records of uterine electromyography signals. Our approach shows an improvement on existing studies with 94.56% for sensitivity, 87.83% for specificity, and 94% for the area under the curve with 9% global error when using the multilayer perceptron neural network trained using the Levenberg-Marquardt algorithm.

Keywords: term delivery; preterm delivery; machine learning; classification; electrohysterography; EHG; artificial neural networks; ANNs; uterine EMG data; electromyography; preterm births; premature delivery; premature babies.

DOI: 10.1504/IJAIS.2015.072143

International Journal of Adaptive and Innovative Systems, 2015 Vol.2 No.2, pp.161 - 179

Received: 06 Aug 2014
Accepted: 08 Apr 2015

Published online: 01 Oct 2015 *

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