通过窗口选择和节点优化来优化妊娠和分娩过程中的子宫同步分析

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2024-06-18 DOI:10.1016/j.irbm.2024.100843
Kamil Bader El Dine , Noujoud Nader , Mohamad Khalil , Catherine Marque
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引用次数: 0

摘要

1) 引言:早产已成为全球 5 岁以下儿童死亡的主要原因。预防早产最重要的关键之一就是及早发现。2) 目标:本研究的主要目的是提供一种新方法,通过分析分娩和怀孕期间记录在母亲腹部的宫颈电图(EHG)信号来解决早产问题。3) 方法:EHG 信号反映了引起子宫肌层机械收缩的电活动。由于已知 EHG 是非稳态信号,而且我们预计连接性会在收缩过程中发生变化(由于电扩散和机械传导过程),因此我们对真实信号采用了开窗法,以确定最佳窗口和具有最重要数据的最佳节点,用于分类。建议的流程包括:i) 将孕妇腹部记录的 16 个 EHG 信号划分为 N 个窗口;ii) 对每个窗口应用连接矩阵;iii) 对每个窗口的连接矩阵应用基于图论的度量;iv) 对每个窗口应用共识矩阵,以检索最佳窗口和最佳节点。然后,根据不同的输入参数(仅连通性方法、连通性方法加图参数、最佳节点、所有节点、最佳窗口、所有窗口),对最佳窗口和最佳节点应用多种神经网络和机器学习方法,对妊娠和分娩宫缩进行分类。4) 结果:结果显示,最佳节点为节点 8、9、10、11 和 12;最佳窗口为 2、4 和 5。仅使用这些最佳节点获得的分类结果比使用全部节点获得的结果要好。无论选择哪个节点,使用全脉冲串的结果总是更好。5) 结论:事实证明,开窗法是一种创新技术,可以提高对分娩和妊娠超高频信号的区分度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Optimizing Uterine Synchronization Analysis in Pregnancy and Labor Through Window Selection and Node Optimization

Optimizing Uterine Synchronization Analysis in Pregnancy and Labor Through Window Selection and Node Optimization

1) Introduction: Preterm labor (PL) has globally become the leading cause of death in children under the age of 5 years. One of the most significant keys to preventing preterm labor is its early detection. 2) Objectives: The primary objectives of this study are to address the problem of PL by providing a new approach by analyzing the electrohysterographic (EHG) signals, which are recorded on the mother's abdomen during labor and pregnancy. 3) Methods: The EHG signal reflects the electrical activity that induces the mechanical contraction of the myometrium. Because EHGs are known to be non-stationary signals, and because we anticipate connectivity to alter during contraction (due to electrical diffusion and the mechanotransduction process), we applied the windowing approach on real signals to identify the best windows and the best nodes with the most significant data to be used for classification. The suggested pipeline includes: i) dividing the 16 EHG signals that are recorded from the abdomen of pregnant women in N windows; ii) apply the connectivity matrices on each window; iii) apply the Graph theory-based measures on the connectivity matrices on each window; iv) apply the consensus Matrix on each window in order to retrieve the best windows and the best nodes. Following that, several neural network and machine learning methods are applied to the best windows and best nodes to categorize pregnancy and labor contractions, based on the different input parameters (connectivity method alone, connectivity method plus graph parameters, best nodes, all nodes, best windows, all windows). 4) Results: Results showed that the best nodes are nodes 8, 9, 10, 11, and 12; while the best windows are 2, 4, and 5. The classification results obtained by using only these best nodes are better than when using the whole nodes. The results are always better when using the full burst, whatever the chosen nodes. 5) Conclusion: The windowing approach proved to be an innovative technique that can improve the differentiation between labor and pregnancy EHG signals.

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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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