NDLI: Prediction of labor using non-invasive laplacian EHG recordings

Content Provider	IEEE Xplore Digital Library
Author	Ye-Lin, Y. Prats-Boluda, G. Alberola-Rubio, J. Bueno Barrachina, J.-M. Perales, A. Garcia-Casado, J.
Copyright Year	2013
Description	Author affiliation: Grupo de Bioelectronica, Univ. Politec. de Valencia, Valencia, Spain (Ye-Lin, Y.; Prats-Boluda, G.; Alberola-Rubio, J.; Bueno Barrachina, J.-M.; Garcia-Casado, J.) \|\| Servicio de Obstetricia, H.U. La Fe, Valencia, Spain (Perales, A.)
Abstract	Non-invasive electrohysterogram (EHG) recordings could be used as an alternative technique for monitoring uterine dynamics. Bipolar recordings of EHG have proven to provide valuable information to predict labor. Recently it has been stated that uterine EHG bursts could also be identified in Laplacian recordings on abdominal surface. Taking into account that Laplacian potential technique permits to acquire more localized electrical activity than conventional recordings; these recordings could also be helpful for deducing uterine contraction efficiency. The aim of this paper is to examine the feasibility of Laplacian potential EHG recording for labor prediction and to compare it with monopolar recordings. To this purpose, a total of 42 EHG recordings were acquired from women of similar gestational age: 29 antepartum patients, and 13 patients in labor. Then linear and non-linear classifiers have been implemented using EHG burst parameters as input features. Experimental results show significant differences in temporal and spectral parameters in both monopolar and Laplacian potential recordings between the two groups. In addition, support vector machine based classifier achieved an accuracy of 93% for labor prediction for monopolar recordings, 92% for bipolar recordings and 91% for Laplacian potential.
Starting Page	7428
Ending Page	7431
File Size	300836
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6611275
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Laplace equations Accuracy Electric potential Electrodes Support vector machines Pregnancy Monitoring
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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