Characteristics of phase synchronization in electrohysterography and tocodynamometry for preterm birth prediction
Preterm birth prediction is important in prenatal care; however, it remains a significant challenge due to the complex physiological mechanisms involved. This study aimed to explore the feasibility of phase synchronization of multiple oscillatory components across electrohysterography (EHG) and toco...
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Elsevier
2024-11-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402416464X |
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| author | Jae-Hwan Kang Young-Ju Jeon In-Seon Lee Junsuk Kim |
| author_facet | Jae-Hwan Kang Young-Ju Jeon In-Seon Lee Junsuk Kim |
| author_sort | Jae-Hwan Kang |
| collection | DOAJ |
| description | Preterm birth prediction is important in prenatal care; however, it remains a significant challenge due to the complex physiological mechanisms involved. This study aimed to explore the feasibility of phase synchronization of multiple oscillatory components across electrohysterography (EHG) and tocodynamometry (TOCO) signals to identify preterm births using advanced machine-learning techniques. Using an open-access EHG dataset, we first assessed the degree of phase synchronization of five specified frequency ranges from 0.08 to 5.0 Hz in three individual EHG signals by constructing two distinct sets of mean phase coherence: the inclusion or exclusion of TOCO signals. We then employed two machine-learning models, XGBoost and TabNet, to classify preterm and term delivery conditions and analyze the predictive potential of these features. The models’ performance was evaluated by considering varying lengths of time windows and the use of overlapping windows. Our results demonstrate the importance of lower-frequency EHG signals and synchronization patterns across the horizontal plane of the abdomen, particularly synchronization between the upper and lower regions of the uterus. Furthermore, we observed a distinctive pattern in the high-frequency band (1.0–2.2 Hz), emphasizing the important role of the lower horizontal regions with other sites in the synchronization process. Interestingly, our findings indicated that TOCO signals, while not substantially enhancing the overall prediction performance, contributed to slightly improved accuracy rates when combined with EHG signals. This study suggests the critical role of EHG signals and their intricate spatiotemporal patterns in predicting preterm birth, providing insights for the development of more accurate and efficient prediction models. |
| format | Article |
| id | doaj-art-c1eccee3673c4838b7a95aa1e0c897e6 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
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| series | Heliyon |
| spelling | doaj-art-c1eccee3673c4838b7a95aa1e0c897e62024-11-30T07:12:59ZengElsevierHeliyon2405-84402024-11-011022e40433Characteristics of phase synchronization in electrohysterography and tocodynamometry for preterm birth predictionJae-Hwan Kang0Young-Ju Jeon1In-Seon Lee2Junsuk Kim3Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, South Korea; Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, South KoreaDigital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, South Korea; Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, South KoreaCollege of Korean Medicine, Kyung Hee University, Seoul, South KoreaSchool of Information Convergence, Kwangwoon University, Seoul, South Korea; Corresponding author. School of Information Convergence, College of AI Convergence, Kwangwoon University, Nowon-gu, 01897, Seoul, South Korea.Preterm birth prediction is important in prenatal care; however, it remains a significant challenge due to the complex physiological mechanisms involved. This study aimed to explore the feasibility of phase synchronization of multiple oscillatory components across electrohysterography (EHG) and tocodynamometry (TOCO) signals to identify preterm births using advanced machine-learning techniques. Using an open-access EHG dataset, we first assessed the degree of phase synchronization of five specified frequency ranges from 0.08 to 5.0 Hz in three individual EHG signals by constructing two distinct sets of mean phase coherence: the inclusion or exclusion of TOCO signals. We then employed two machine-learning models, XGBoost and TabNet, to classify preterm and term delivery conditions and analyze the predictive potential of these features. The models’ performance was evaluated by considering varying lengths of time windows and the use of overlapping windows. Our results demonstrate the importance of lower-frequency EHG signals and synchronization patterns across the horizontal plane of the abdomen, particularly synchronization between the upper and lower regions of the uterus. Furthermore, we observed a distinctive pattern in the high-frequency band (1.0–2.2 Hz), emphasizing the important role of the lower horizontal regions with other sites in the synchronization process. Interestingly, our findings indicated that TOCO signals, while not substantially enhancing the overall prediction performance, contributed to slightly improved accuracy rates when combined with EHG signals. This study suggests the critical role of EHG signals and their intricate spatiotemporal patterns in predicting preterm birth, providing insights for the development of more accurate and efficient prediction models.http://www.sciencedirect.com/science/article/pii/S240584402416464XElectrohysterogramPreterm birth predictionPhase synchronizationMean phase coherence |
| spellingShingle | Jae-Hwan Kang Young-Ju Jeon In-Seon Lee Junsuk Kim Characteristics of phase synchronization in electrohysterography and tocodynamometry for preterm birth prediction Heliyon Electrohysterogram Preterm birth prediction Phase synchronization Mean phase coherence |
| title | Characteristics of phase synchronization in electrohysterography and tocodynamometry for preterm birth prediction |
| title_full | Characteristics of phase synchronization in electrohysterography and tocodynamometry for preterm birth prediction |
| title_fullStr | Characteristics of phase synchronization in electrohysterography and tocodynamometry for preterm birth prediction |
| title_full_unstemmed | Characteristics of phase synchronization in electrohysterography and tocodynamometry for preterm birth prediction |
| title_short | Characteristics of phase synchronization in electrohysterography and tocodynamometry for preterm birth prediction |
| title_sort | characteristics of phase synchronization in electrohysterography and tocodynamometry for preterm birth prediction |
| topic | Electrohysterogram Preterm birth prediction Phase synchronization Mean phase coherence |
| url | http://www.sciencedirect.com/science/article/pii/S240584402416464X |
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