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Evaluation of advanced artificial neural network classification and feature extraction techniques for detecting preterm births using ehg records

Fergus, P and Idowu, IO and Hussain, A and Dobbins, C and Al-Askar, H (2014) Evaluation of advanced artificial neural network classification and feature extraction techniques for detecting preterm births using ehg records. Intelligent Computing in Bioinformatics: Lecture Notes in Computer Science, 8590. pp. 309-314. ISSN 0302-9743

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Abstract

Globally, the rate of preterm births is increasing and this is resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. However, there has been some evidence to suggest 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. In this paper however, the electrohysterography signals have been used to detect preterm births. This has been achieved using an open dataset that 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 to determine their discriminative capabilities in detecting term and preterm records. Seven artificial neural network algorithms are considered with the results showing that the Radial Basis Function Neural Network classifier performs the best, with 85% sensitivity, 80% specificity, 90% area under the curve and a 17% mean error rate. © 2014 Springer International Publishing Switzerland.

Item Type: Article
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-09330-7-37
Uncontrolled Keywords: 08 Information And Computing Sciences
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
R Medicine > RG Gynecology and obstetrics
Divisions: Computer Science
Publisher: Springer Verlag
Date Deposited: 25 Nov 2015 07:33
Last Modified: 13 Feb 2017 16:37
DOI or Identification number: 10.1007/978-3-319-09330-7-37
URI: http://researchonline.ljmu.ac.uk/id/eprint/2375

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