Literature DB >> 10513127

Use of the electrohysterogram signal for characterization of contractions during pregnancy.

H Leman1, C Marque, J Gondry.   

Abstract

This article proposes a method to evaluate the ability of the electrohysterogram signal to characterize the contractions during pregnancy, in a population with high risk of preterm deliveries. This study constitutes a first stage of a project intended to develop a monitoring system for the early diagnosis of preterm deliveries. After a proper signal denoising, we calculate some parameters characteristic of the extracted contractions. These contractions are then divided into classes of different physiological terms. Classical techniques of data analysis, such as principal component analysis and discriminant analysis, permit us to show an evolution of the contractions during pregnancy, which is different between the groups of preterm deliveries and that of deliveries at term. We show that, in an early term of pregnancy, we can separate the two populations: women delivering at term from women delivering preterm. We then show that these two kinds of pregnancy are of different evolutions. These results are encouraging, because they would permit, in a follow-up medical study, to diagnose a possible preterm delivery, as well as the proximity of the delivery.

Entities:  

Mesh:

Year:  1999        PMID: 10513127     DOI: 10.1109/10.790499

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  15 in total

1.  A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups.

Authors:  G Fele-Zorz; G Kavsek; Z Novak-Antolic; F Jager
Journal:  Med Biol Eng Comput       Date:  2008-04-24       Impact factor: 2.602

Review 2.  Use of uterine electromyography to diagnose term and preterm labor.

Authors:  Miha Lucovnik; Ruben J Kuon; Linda R Chambliss; William L Maner; Shao-Qing Shi; Leili Shi; James Balducci; Robert E Garfield
Journal:  Acta Obstet Gynecol Scand       Date:  2010-12-07       Impact factor: 3.636

Review 3.  Use of Non-invasive Uterine Electromyography in the Diagnosis of Preterm Labour.

Authors:  M Lucovnik; Z Novak-Antolic; R E Garfield
Journal:  Facts Views Vis Obgyn       Date:  2012

4.  Prediction of preterm deliveries from EHG signals using machine learning.

Authors:  Paul Fergus; Pauline Cheung; Abir Hussain; Dhiya Al-Jumeily; Chelsea Dobbins; Shamaila Iram
Journal:  PLoS One       Date:  2013-10-28       Impact factor: 3.240

5.  Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.

Authors:  Paul Fergus; David Hignett; Abir Hussain; Dhiya Al-Jumeily; Khaled Abdel-Aziz
Journal:  Biomed Res Int       Date:  2015-01-29       Impact factor: 3.411

Review 6.  Electrodes in external electrohysterography: a systematic literature review.

Authors:  Thierry R Jossou; Aziz Et-Tahir; Zakaria Tahori; Abdelmajid El Ouadi; Daton Medenou; Abdelmajid Bybi; Latif Fagbemi; Mohamed Sbihi; Davide Piaggio
Journal:  Biophys Rev       Date:  2021-05-09

7.  Preterm labour detection by use of a biophysical marker: the uterine electrical activity.

Authors:  Catherine K Marque; Jérémy Terrien; Sandy Rihana; Guy Germain
Journal:  BMC Pregnancy Childbirth       Date:  2007-06-01       Impact factor: 3.007

8.  Automatic identification of motion artifacts in EHG recording for robust analysis of uterine contractions.

Authors:  Yiyao Ye-Lin; Javier Garcia-Casado; Gema Prats-Boluda; José Alberola-Rubio; Alfredo Perales
Journal:  Comput Math Methods Med       Date:  2014-01-09       Impact factor: 2.238

9.  Assessment of parturition with cervical light-induced fluorescence and uterine electromyography.

Authors:  Miha Lucovnik; Ruben J Kuon; Robert E Garfield
Journal:  Comput Math Methods Med       Date:  2013-09-29       Impact factor: 2.238

10.  Velocity and directionality of the electrohysterographic signal propagation.

Authors:  Lasse Lange; Anders Vaeggemose; Preben Kidmose; Eva Mikkelsen; Niels Uldbjerg; Peter Johansen
Journal:  PLoS One       Date:  2014-01-21       Impact factor: 3.240

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