Identification of Contractions from Electrohysterography for Prediction of Prolonged Labor.

Q3 Biochemistry, Genetics and Molecular Biology
Journal of Electrical Bioimpedance Pub Date : 2022-03-31 eCollection Date: 2022-01-01 DOI:10.2478/joeb-2022-0002
Santosh N Vasist, Parvati Bhat, Shrutin Ulman, Harishchandra Hebbar
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引用次数: 0

Abstract

The analysis of the uterine electrical activity and its propagation patterns could potentially predict the risk of prolonged/arrested progress of labor. In our study, the Electrohysterography (EHG) signals of 83 participants in labor at around 3-4 cm of cervical dilatation, were recorded for about 30 minutes each. These signals were analyzed for predicting prolonged labor. Out of the 83 participants, 70 participants had normal progress of labor and delivered vaginally. The remaining 13 participants had prolonged/ arrested progress of labor and had to deliver through a cesarean section. In this paper, we propose an algorithm to identify contractions from the acquired EHG signals based on the energy of the signals. The role of contraction consistency and fundal dominance was evaluated for impact on progress of the labor. As per our study, the correlation of contractions was higher in case of normal progress of labor. We also observed that the upper uterine segment was dominant in cases with prolonged/arrested progress of labor.

通过宫电图识别宫缩预测产程延长
子宫电活动及其传播模式的分析可以潜在地预测分娩过程延长/停滞的风险。在我们的研究中,记录了83名分娩参与者在宫颈扩张约3-4厘米时的子宫电图(EHG)信号,每个人约30分钟。分析这些信号以预测分娩时间延长。在83名参与者中,70名参与者产程正常,顺产。其余13名参与者的分娩过程延长或停滞,不得不通过剖宫产进行分娩。在本文中,我们提出了一种基于信号能量的收缩识别算法。评估宫缩一致性和基底优势对产程的影响。根据我们的研究,在产程正常的情况下,宫缩的相关性更高。我们还观察到,在分娩过程延长/停滞的病例中,子宫上段占主导地位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Electrical Bioimpedance
Journal of Electrical Bioimpedance Engineering-Biomedical Engineering
CiteScore
3.00
自引率
0.00%
发文量
8
审稿时长
17 weeks
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