Uterine Synchronization Analysis During Pregnancy and Labor Using Graph Theory, Classification Based on Neural Network and Deep Learning - 20/09/22
Graphical abstract |
Highlights |
• | EHG signals recorded from women's abdomen to measure the uterine electrical activity. |
• | Connectivity and graph methods applied on EHGs to study the propagation in the uterus. |
• | Artificial intelligence methods applied to classify between pregnancy and labor. |
Abstract |
1) Objectives |
Preterm birth caused by preterm labor is one of the major health problems in the world. In this article, we present a new framework for dealing with this problem through the processing of electrohysterographic signals (EHG) that are recorded during labor and pregnancy. The objective in this research is to improve the classification between labor and pregnancy contractions by using a new approach that focuses on the connectivity analysis based on graph parameters, representative of uterine synchronization, and comparing neural network and machine learning methods in order to classify between labor and pregnancy.
2) Material and methods |
after denoising of the 16 EHG signals recorded from pregnant women abdomen, we applied different connectivity methods to obtain connectivity matrices; then by using the graph theory, we extracted some graph parameters from the connectivity matrices; finally, we tested different neural network and machine learning methods on the features obtained from both graph and connectivity methods in order to classify between labor and pregnancy.
3) Results |
The best results were obtained by using the logistic regression method. We also evidence the power of graph parameters extracted from the connectivity matrices to improve the classification results.
4) Conclusion |
The use of graph analysis associated with machine learning methods can be a powerful tool to improve labor and pregnancy classification based on the analysis of EHG signals.
Le texte complet de cet article est disponible en PDF.Keywords : Preterm labor, Neural network, Connectivity methods, Graph theory, Deep learning
Plan
Vol 43 - N° 5
P. 333-339 - octobre 2022 Retour au numéroBienvenue sur EM-consulte, la référence des professionnels de santé.
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