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Advanced Artificial Neural Network Classification for Detecting Preterm Births Using EHG Records

Fergus, P and Hussain, A and Al-Jumeily, D and Idowu, IO and Al-Askar, H (2015) Advanced Artificial Neural Network Classification for Detecting Preterm Births Using EHG Records. NEUROCOMPUTING. ISSN 0925-2312

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Abstract

Globally, the rate of preterm births are increasing, thus resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. Nevertheless, there has been some evidence that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect when preterm delivery is about to occur. Using advanced machine learning algorithms, in conjunction with Electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. However, in this paper, the Electrohysterography signals have been used to detect preterm births. This has been achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies have been utilized, as well as feature-ranking techniques. Features are ranked to determine their discriminative capabilities in detecting term and preterm records. Seven different artificial neural networks were then used to identify these records. The results illustrate that the Radial Basis Function Neural Network classifier performed the best, with 85% sensitivity, 80% specificity, 90% area under the curve and a 17% mean error rate.

Item Type: Article
Uncontrolled Keywords: 08 Information And Computing Sciences, 09 Engineering, 17 Psychology And Cognitive Sciences
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RG Gynecology and obstetrics
Divisions: Computer Science
Publisher: ELSEVIER SCIENCE BV
Date Deposited: 25 Nov 2015 14:30
Last Modified: 29 Nov 2016 00:50
Editors: Heskes, T
URI: http://researchonline.ljmu.ac.uk/id/eprint/2373

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