Simulated uterine contractions: Graph theory and connectivity-based analysis of EHG signals
Preterm labor represents the prominent cause of mortality and morbidity, highlighting the important need for improved preterm contraction prediction and management. One promising approach to resolving this challenge is to analyze the electrohysterographic (EHG) signal, which records the electrical a...
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Elsevier
2025-06-01
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| Series: | Biomedical Engineering Advances |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667099225000349 |
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| author | Kamil Bader Eldine Noujoud Nader Mohamad Khalil Catherine Marque |
| author_facet | Kamil Bader Eldine Noujoud Nader Mohamad Khalil Catherine Marque |
| author_sort | Kamil Bader Eldine |
| collection | DOAJ |
| description | Preterm labor represents the prominent cause of mortality and morbidity, highlighting the important need for improved preterm contraction prediction and management. One promising approach to resolving this challenge is to analyze the electrohysterographic (EHG) signal, which records the electrical activity regulating uterine contractions. Analyzing the features of the EHG signal contributes valuable data to detect labor. In this paper, we propose a new framework using simulated EHG signals to identify features sensitive to uterine connectivity. We focus on EHG signal propagation during labor, recorded by multiple electrodes. We simulated EHG signals in different groups to determine which connectivity methods and graph parameters best represent the two main factors driving uterine synchronization: short-distance propagation (via electrical diffusion, ED) and long-distance synchronization (via mechanotransduction, EDM). Using the uterine model, signals were first simulated using just electrical diffusion by modifying the tissue resistance; second, signals were simulated using ED and mechanotransduction by holding the tissue resistance constant and varying the model parameters that affect mechanotransduction. We used the bipolar technique to construct our simulated EHGs by modeling a matrix of 16 surface electrodes organized in a 4 × 4 matrix placed on the pregnant woman’s abdomen. Our results show that even a simplified electromechanical model can be useful for monitoring uterine synchronization using simulated EHG signals. The differences seen between the selection performed by Fscore on real and simulated EHG signals show that when employing the mean function, the best features are H2(Str), FW_h2 alone, and in combination with PR, BC, and CC. The best characteristics that demonstrate a shift in the mechanotransduction process are H2 alone or in combination with Str, R2(PR), and ICOH(Str). The best characteristics that demonstrate a shift in electrical diffusion are H2 alone and in combination with Eff, PR, and BC. |
| format | Article |
| id | doaj-art-a093e5c0edcd43d1ba388570af91085d |
| institution | DOAJ |
| issn | 2667-0992 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Biomedical Engineering Advances |
| spelling | doaj-art-a093e5c0edcd43d1ba388570af91085d2025-08-20T03:20:55ZengElsevierBiomedical Engineering Advances2667-09922025-06-01910017810.1016/j.bea.2025.100178Simulated uterine contractions: Graph theory and connectivity-based analysis of EHG signalsKamil Bader Eldine0Noujoud Nader1Mohamad Khalil2Catherine Marque3CNRS UMR 7338, BMBI Sorbonne University, Université de technologie de Compiègne, Compiègne, France; Faculty of engineering, Azm center for research in biotechnology, Lebanese University, Lebanon; Corresponding author at: Universite de Technologie de Compiegne, Lebanon.Louisiana State University, Baton Rouge, LA, USA; Faculty of engineering, Azm center for research in biotechnology, Lebanese University, LebanonFaculty of engineering, Azm center for research in biotechnology, Lebanese University, LebanonCNRS UMR 7338, BMBI Sorbonne University, Université de technologie de Compiègne, Compiègne, FrancePreterm labor represents the prominent cause of mortality and morbidity, highlighting the important need for improved preterm contraction prediction and management. One promising approach to resolving this challenge is to analyze the electrohysterographic (EHG) signal, which records the electrical activity regulating uterine contractions. Analyzing the features of the EHG signal contributes valuable data to detect labor. In this paper, we propose a new framework using simulated EHG signals to identify features sensitive to uterine connectivity. We focus on EHG signal propagation during labor, recorded by multiple electrodes. We simulated EHG signals in different groups to determine which connectivity methods and graph parameters best represent the two main factors driving uterine synchronization: short-distance propagation (via electrical diffusion, ED) and long-distance synchronization (via mechanotransduction, EDM). Using the uterine model, signals were first simulated using just electrical diffusion by modifying the tissue resistance; second, signals were simulated using ED and mechanotransduction by holding the tissue resistance constant and varying the model parameters that affect mechanotransduction. We used the bipolar technique to construct our simulated EHGs by modeling a matrix of 16 surface electrodes organized in a 4 × 4 matrix placed on the pregnant woman’s abdomen. Our results show that even a simplified electromechanical model can be useful for monitoring uterine synchronization using simulated EHG signals. The differences seen between the selection performed by Fscore on real and simulated EHG signals show that when employing the mean function, the best features are H2(Str), FW_h2 alone, and in combination with PR, BC, and CC. The best characteristics that demonstrate a shift in the mechanotransduction process are H2 alone or in combination with Str, R2(PR), and ICOH(Str). The best characteristics that demonstrate a shift in electrical diffusion are H2 alone and in combination with Eff, PR, and BC.http://www.sciencedirect.com/science/article/pii/S2667099225000349Preterm laborConnectivity methodsSimulated signalsGraph metricsElectrical diffusionMechanotransduction |
| spellingShingle | Kamil Bader Eldine Noujoud Nader Mohamad Khalil Catherine Marque Simulated uterine contractions: Graph theory and connectivity-based analysis of EHG signals Biomedical Engineering Advances Preterm labor Connectivity methods Simulated signals Graph metrics Electrical diffusion Mechanotransduction |
| title | Simulated uterine contractions: Graph theory and connectivity-based analysis of EHG signals |
| title_full | Simulated uterine contractions: Graph theory and connectivity-based analysis of EHG signals |
| title_fullStr | Simulated uterine contractions: Graph theory and connectivity-based analysis of EHG signals |
| title_full_unstemmed | Simulated uterine contractions: Graph theory and connectivity-based analysis of EHG signals |
| title_short | Simulated uterine contractions: Graph theory and connectivity-based analysis of EHG signals |
| title_sort | simulated uterine contractions graph theory and connectivity based analysis of ehg signals |
| topic | Preterm labor Connectivity methods Simulated signals Graph metrics Electrical diffusion Mechanotransduction |
| url | http://www.sciencedirect.com/science/article/pii/S2667099225000349 |
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