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Idowu, IO, Fergus, P, Hussain, A, Dobbins, C and Al-Askar, H (2014) Advance Artificial Neural Network Classification Techniques Using EHG for Detecting Preterm Births. In: 2014 EIGHTH INTERNATIONAL CONFERENCE ON COMPLEX, INTELLIGENT AND SOFTWARE INTENSIVE SYSTEMS (CISIS), . (8th International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), 02 July 2014 - 04 July 2014, Birmingham City Univ, Birmingham, UNITED KINGDOM).
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Abstract
Worldwide the rate of preterm birth is increasing, which presents significant health, developmental and economic problems. Current methods for predicting preterm births at an early stage are inadequate. Yet, there has been increasing evidence that the analysis of uterine electrical signals, from the abdominal surface, could provide an independent and easy way to diagnose true labour and predict preterm delivery. This analysis provides a heavy focus on the use of advanced machine learning techniques and Electrohysterography (EHG) signal processing. Most EHG studies have focused on true labour detection, in the window of around seven days before labour. However, this paper focuses on using such EHG signals to detect preterm births. In achieving this, the study uses an open dataset containing 262 records for women who delivered at term and 38 who delivered prematurely. The synthetic minority oversampling technique is utilized to overcome the issue with imbalanced datasets to produce a dataset containing 262 term records and 262 preterm records. Six different artificial neural networks were used to detect term and preterm records. The results show that the best performing classifier was the LMNC with 96% sensitivity, 92% specificity, 95% AUC and 6% mean error.
| Item Type: | Conference or Workshop Item (Paper) |
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| Uncontrolled Keywords: | Science & Technology; Technology; Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Computer Science, Theory & Methods; Computer Science; Electrohysterography(EHG); Preterm Delivery; Term Delivery; Classification; artificial neural networks; AUC; ROC and Features extraction; DELIVERY; LABOR; MODELS; TERM |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Computer Science & Mathematics |
| Publisher: | IEEE |
| Related URLs: | |
| Date Deposited: | 27 Nov 2015 09:17 |
| Last Modified: | 13 Apr 2022 15:14 |
| DOI or ID number: | 10.1109/CISIS.2014.14 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/2396 |
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