Advanced artificial neural network classification for detecting preterm births using EHG records

NEUROCOMPUTING

卷:188

Article

Fergus, Paul¹  Idowu, Ibrahim¹  Hussain, Abir¹  Dobbins, Chelsea¹ 

[1] Liverpool John Moores Univ, Sch Comp Sci, Liverpool L3 3AF, Merseyside, England

关键词: Electrohysterography (EHG);    Classification;    Artificial Neural Networks;    Area Under the Curve (AUC);    Receiver Operating Curve (ROC);    Feature Extraction;   

DOI  :  10.1016/j.neucom.2015.01.107

来源: Elsevier

【 摘 要 】

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 the onset of preterm delivery. 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 utilised, as well as feature-ranking techniques 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 combination of the Levenberg-Marquardt trained Feed-Forward Neural Network, Radial Basis Function Neural Network and the Random Neural Network classifiers performed the best, with 91% for sensitivity, 84% for specificity, 94% for the area under the curve and 12% for the mean error rate. (C) 2015 Elsevier B.V. All rights reserved.

【 授权许可】

Free