Fergus, P, Hussain, A and Al-Jumeily, D (2015) A Machine Learning System for Automatic Detection of Preterm Activity Using Artificial Neural Networks and Uterine Electromyography Data. International Journal of Adaptive and Innovative Systems, 2 (2). pp. 161-179. ISSN 1740-2115

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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.

Item Type:	Article
Uncontrolled Keywords:	0915 Interdisciplinary Engineering
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RG Gynecology and obstetrics
Divisions:	Computer Science & Mathematics
Date Deposited:	25 Nov 2015 12:32
Last Modified:	09 Mar 2022 11:32
URI:	https://researchonline.ljmu.ac.uk/id/eprint/2383

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