Graph analysis of uterine networks using EHG source connectivity

Emerging evidence show that the connectivity analysis of the uterine signals is a powerful tool in characterizing pregnancy and labor contractions. Here, we present the results of studying the connectivity between uterine sources identified from the electrohysterogram (EHG) signals, which reflects the electrical activity of the uterine muscle. We started by evaluating the effect of the two key steps involved in EHG source connectivity processing: i) the algorithm used in the solution of the inverse problem and ii) the method used for the estimation of the functional connectivity. We evaluate three different inverse solutions (to reconstruct the dynamics of uterine sources) and three connectivity measures (to compute statistical couplings between the reconstructed sources). The networks obtained by each combination of the inverse/connectivity methods were compared to a reference network (ground truth) generated by the model. The method was then applied to real EHG signals in order to discriminate pregnancy and labor contractions.

[1]  Mahmoud Hassan,et al.  Combination of Canonical Correlation Analysis and Empirical Mode Decomposition Applied to Denoising the Labor Electrohysterogram , 2011, IEEE Transactions on Biomedical Engineering.

[2]  Ahmad Diab,et al.  Dynamic Behavior of Uterine Contractions: An Approach Based on Source Localization and Multiscale Modeling , 2014, KSE.

[3]  C Marque,et al.  Uterine electromyography: a critical review. , 1993, American journal of obstetrics and gynecology.

[4]  Mahmoud Hassan,et al.  Identification of Interictal Epileptic Networks from Dense-EEG , 2016, Brain Topography.

[5]  Leslie Sue Lieberman,et al.  The logic of life: A History of Heredity. By F. Jacob. x + 348 pp. Princeton: Princeton University Press. 1993. $12.95 (paper) , 1993, American journal of human biology : the official journal of the Human Biology Council.

[6]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[7]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[8]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[9]  Olaf Hauk,et al.  Comparison of noise-normalized minimum norm estimates for MEG analysis using multiple resolution metrics , 2011, NeuroImage.

[10]  M. Lucovnik,et al.  Noninvasive uterine electromyography for prediction of preterm delivery. , 2011, American journal of obstetrics and gynecology.

[11]  Catherine Marque,et al.  An electro-mechanical multiscale model of uterine pregnancy contraction , 2016, Comput. Biol. Medicine.

[12]  Jan W. M. Bergmans,et al.  Noninvasive Estimation of the Electrohysterographic Action-Potential Conduction Velocity , 2010, IEEE Transactions on Biomedical Engineering.

[13]  C Marque,et al.  Human abdominal EHG processing for uterine contraction monitoring. , 1989, Biotechnology.

[14]  C. Marque,et al.  Abdominal EHG on a 4 by 4 grid: mapping and presenting the propagation of uterine contractions , 2007 .

[15]  Brynjar Karlsson,et al.  Better Pregnancy Monitoring Using Nonlinear Correlation Analysis of External Uterine Electromyography , 2013, IEEE Transactions on Biomedical Engineering.

[16]  Mahmoud Hassan,et al.  Interactions between Uterine EMG at Different Sites Investigated Using Wavelet Analysis: Comparison of Pregnancy and Labor Contractions , 2010, EURASIP J. Adv. Signal Process..

[17]  Théodore Papadopoulo,et al.  OpenMEEG: opensource software for quasistatic bioelectromagnetics , 2010, Biomedical engineering online.

[18]  M. Khalil,et al.  A novel algorithm for measuring graph similarity: Application to brain networks , 2015, 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER).