Nieto Del-Amor, F.; Prats-Boluda, G.; Li, W.; Martínez-De-Juan, JL.; Yang, L.; Yang, Y.; Hao, D.... (2024). Recurrence quantification analysis of uterine vectormyometriogram to identify pregnant women with threatened preterm labor. Biomedical Signal Processing and Control. 89. https://doi.org/10.1016/j.bspc.2023.105795
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/204776
Título:
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Recurrence quantification analysis of uterine vectormyometriogram to identify pregnant women with threatened preterm labor
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Autor:
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Nieto del-Amor, Félix
Prats-Boluda, Gema
Li, Wanting
Martínez-de-Juan, José L.
Yang, Lin
Yang, Yongxiu
Hao, Dongmei
Ye Lin, Yiyao
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Entidad UPV:
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Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería del Diseño - Escola Tècnica Superior d'Enginyeria del Disseny
Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials
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Fecha difusión:
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Resumen:
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[EN] Electrohysterography has been shown to provide relevant information on preventing preterm labor. Recent studies have confirmed the feasibility of using the vectormyometriogram (VMG) to assess uterine myoelectric vector ...[+]
[EN] Electrohysterography has been shown to provide relevant information on preventing preterm labor. Recent studies have confirmed the feasibility of using the vectormyometriogram (VMG) to assess uterine myoelectric vector displacement, with different physiological implications for the slow and fast waves, without suggesting its implementation in clinical practice. The fast wave VMG component has dynamic behavior in any specific direction on the X-Y plane. Since recurrence is a common feature of dynamic systems, we aimed to determine the recurrence pattern of uterine vector displacement, exploring its clinical potential in detecting imminent and preterm labor in women with threatened preterm labor and a serious preterm birth risk. For this, we analyzed the recurrence patterns from a 2D-vectormyometriogram using four common statistics: determinism, longest diagonal, entropy, and laminarity. We found significantly increased determinism (0.035 ± 0.011 vs. 0.077 ± 0.041), entropy (1.768 ± 0.116 vs. 2.197 ± 0.24) and laminarity (0.086 ± 0.034 vs. 0.173 ± 0.078) from the early (26¿30 weeks) to late (35¿37 weeks) gestation stages. As pregnancy progresses, the uterine vector displacement becomes more periodic, predictable and stable, while VMG recurrence statistics in the fast wave high bandwidth better detect imminent and preterm labor, outperforming classical EHG parameters from bipolar channels. The proposed method was also resistant to motion artifacts and preserved its discriminative capacity between the groups. Our results on VMG recurrence statistics could thus be another reliable biomarker for preventing preterm labor in women with threatened preterm labor and would favor transferring the EHG technique to clinical practice.
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Palabras clave:
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Preterm labor
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Vectormyometriogram
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RQA
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Recurrence plot
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Electrohysterography
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Derechos de uso:
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Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
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Fuente:
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Biomedical Signal Processing and Control. (issn:
1746-8094
)
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DOI:
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10.1016/j.bspc.2023.105795
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Editorial:
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Elsevier
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Versión del editor:
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https://doi.org/10.1016/j.bspc.2023.105795
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Código del Proyecto:
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info:eu-repo/grantAgreement/AEI//PID2021-124038OB-I00//INTELIGENCIA ARTIFICIAL PARA LA AYUDA AL DIAGNÓSTICO EN TIEMPO REAL DEL PARTO PREMATURO BASADO EN LA ACTIVIDAD MIOELÉCTRICA UTERINA. ÉNFASIS
EN GESTACIONES MÚLTIPLES/
info:eu-repo/grantAgreement/NSFC//U20A20388/
info:eu-repo/grantAgreement/NKRDPC//2019YFC0119700/
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Agradecimientos:
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This work was supported by the Spanish Ministry of Science and Innovation and the European Regional Development Fund, State Plan for Scientific, Technical and Innovation Research 2021 - 2023 (PID2021-124038OB-I00) . This ...[+]
This work was supported by the Spanish Ministry of Science and Innovation and the European Regional Development Fund, State Plan for Scientific, Technical and Innovation Research 2021 - 2023 (PID2021-124038OB-I00) . This research was funded by the National Key R & D Program, grant number 2019YFC0119700, and the National Natural Science Foundation of China, grant number U20A20388.
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Tipo:
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Artículo
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