Next Article in Journal
Design and Experimental Investigation of Thermosiphoning Heat Transfer through Nanofluids in Compound Parabolic Collector
Previous Article in Journal
Accuracy Assessment of TanDEM-X 90 and CartoDEM Using ICESat-2 Datasets for Plain Regions of Ratlam City and Surroundings
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Engineering Proceedings
Volume 10
Issue 1
10.3390/ecsa-8-11312
engproc-logo
Submit to this Journal
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Proceeding Paper

Pregnancy Labor Prediction Using Magnetomyography Sensing and a Self-Sorting Cybernetic Model †

by
Ejay Nsugbe
1,*,
Oluwarotimi Williams Samuel
2,
Ibrahim Sanusi
3,
Suresh Vishwakarma
4 and
Dawn Adams
5
1
Nsugbe Research Labs, Swindon SN1 3LG, UK
2
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
3
Automatic Control and Systems Engineering, University of Sheffield, Sheffield S10 2TN, UK
4
Utilities Engineering Unit, University of Trinidad and Tobago, 14-24 Keate Street, Port of Spain 100801, Trinidad and Tobago
5
School of Computing, Ulster University, Newtownabbey BT37 0QB, UK
*
Author to whom correspondence should be addressed.
†
Presented at the 8th International Electronic Conference on Sensors and Applications, 1–15 November 2021; Available online: https://ecsa-8.sciforum.net.
Eng. Proc. 2021, 10(1), 60; https://doi.org/10.3390/ecsa-8-11312
Published: 1 November 2021
(This article belongs to the Proceedings of The 8th International Electronic Conference on Sensors and Applications)
Browse Figures
Versions Notes

Abstract

To date, effective means of predicting pregnancy labor continues to lack. Magnetic field signals during uterine contraction have shown, in recent studies, to be a good source of information for predicting labor state with a greater accuracy compared with existing methods. The means of labor prediction methods from such signals appear to rely on a supervised learning post-processing framework whose calibration relies on an effective labelling of the training sample set. As a potential solution to this, using a reduced electrode channel from magnetomyography instrumentation, we propose a multi-stage self-sorting cybernetic model that is comprised of an ensemble of various post-processing methods, and is underpinned by an unsupervised learning framework that allows for an automated method towards learning from the trend in the data to infer labor state and imminency. Experimental results showed a comparable accuracy with those from a supervised learning method adopted in a prior study. Additionally, an architecture of how an intelligent cybernetic model can be used for labor prediction and cost saving benefits within a clinical setting is offered by this study.
Keywords: cybernetics; decision support; biosensors; unsupervised learning; electromagnetism; pregnancy; signal processing; obstetrics; artificial intelligence; intelligent systems cybernetics; decision support; biosensors; unsupervised learning; electromagnetism; pregnancy; signal processing; obstetrics; artificial intelligence; intelligent systems

Share and Cite

MDPI and ACS Style

Nsugbe, E.; Samuel, O.W.; Sanusi, I.; Vishwakarma, S.; Adams, D. Pregnancy Labor Prediction Using Magnetomyography Sensing and a Self-Sorting Cybernetic Model. Eng. Proc. 2021, 10, 60. https://doi.org/10.3390/ecsa-8-11312

AMA Style

Nsugbe E, Samuel OW, Sanusi I, Vishwakarma S, Adams D. Pregnancy Labor Prediction Using Magnetomyography Sensing and a Self-Sorting Cybernetic Model. Engineering Proceedings. 2021; 10(1):60. https://doi.org/10.3390/ecsa-8-11312

Chicago/Turabian Style

Nsugbe, Ejay, Oluwarotimi Williams Samuel, Ibrahim Sanusi, Suresh Vishwakarma, and Dawn Adams. 2021. "Pregnancy Labor Prediction Using Magnetomyography Sensing and a Self-Sorting Cybernetic Model" Engineering Proceedings 10, no. 1: 60. https://doi.org/10.3390/ecsa-8-11312

APA Style

Nsugbe, E., Samuel, O. W., Sanusi, I., Vishwakarma, S., & Adams, D. (2021). Pregnancy Labor Prediction Using Magnetomyography Sensing and a Self-Sorting Cybernetic Model. Engineering Proceedings, 10(1), 60. https://doi.org/10.3390/ecsa-8-11312

Article Metrics

Back to TopTop