A node-wise analysis of the uterine muscle networks for pregnancy monitoring - Université de Rennes Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

A node-wise analysis of the uterine muscle networks for pregnancy monitoring

Résumé

The recent past years have seen a noticeable increase of interest in the correlation analysis of electrohysterographic (EHG) signals in the perspective of improving the pregnancy monitoring. Here we propose a new approach based on the functional connectivity between multichannel (4x4 matrix) EHG signals recorded from the women's abdomen. The proposed pipeline includes i) the computation of the statistical couplings between the multichannel EHG signals, ii) the characterization of the connectivity matrices, computed by using the imaginary part of the coherence, based on the graph-theory analysis and iii) the use of these measures for pregnancy monitoring. The method was evaluated on a dataset of EHGs, in order to track the correlation between EHGs collected by each electrode of the matrix (called 'node-wise' analysis) and follow their evolution along weeks before labor. Results showed that the strength of each node significantly increases from pregnancy to labor. Electrodes located on the median vertical axis of the uterus seemed to be the more discriminant. We speculate that the network-based analysis can be a very promising tool to improve pregnancy monitoring.
Fichier principal
Vignette du fichier
EMBC2016_NaderN.pdf (477.9 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01338181 , version 1 (28-06-2016)

Identifiants

  • HAL Id : hal-01338181 , version 1

Citer

Noujoude Nader, Mahmoud Hassan, Wassim El Falou, Catherine Marque, Mohamad Khalil. A node-wise analysis of the uterine muscle networks for pregnancy monitoring. The 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’16), Aug 2016, Orlando, United States. ⟨hal-01338181⟩
78 Consultations
83 Téléchargements

Partager

Gmail Facebook X LinkedIn More