{"title":"模拟子宫收缩:图论和基于连接的EHG信号分析","authors":"Kamil Bader Eldine , Noujoud Nader , Mohamad Khalil , Catherine Marque","doi":"10.1016/j.bea.2025.100178","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":72384,"journal":{"name":"Biomedical engineering advances","volume":"9 ","pages":"Article 100178"},"PeriodicalIF":0.0000,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Simulated uterine contractions: Graph theory and connectivity-based analysis of EHG signals\",\"authors\":\"Kamil Bader Eldine , Noujoud Nader , Mohamad Khalil , Catherine Marque\",\"doi\":\"10.1016/j.bea.2025.100178\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":72384,\"journal\":{\"name\":\"Biomedical engineering advances\",\"volume\":\"9 \",\"pages\":\"Article 100178\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical engineering advances\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2667099225000349\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical engineering advances","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667099225000349","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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 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.