Lebanese University, Faculty of public health, Beirut, Lebanon; Universités de Sorbonne, Université de Technologie de Compiègne, CNRS-UMR 7338 BMBI, 60200, Compiègne, France; Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.
Universités de Sorbonne, Université de Technologie de Compiègne, CNRS-UMR 7338 BMBI, 60200, Compiègne, France.
Comput Biol Med. 2021 May;132:104308. doi: 10.1016/j.compbiomed.2021.104308. Epub 2021 Mar 6.
Recent years have seen an increased interest in electrohysterogram (EHG) signals as a means to evaluate the synchronization of uterine contractions. Several studies have pointed out that the quality of signal processing - and hence the interpretation of measurement results - is affected significantly by the choice of measurement technique and the presence of non-stationary frequency content in EHG signals. To our knowledge, the effect of time variance on the quality of EHG signal processing has never been fully investigated. How best to process EHG signals with the goal of distinguishing labor-induced contractions from their harmless, pre-labor cousins, remains an open question.
Our methodology is based on three pillars. The first consists of a new method for EHG preprocessing in which we apply a second-order Butterworth filter to retain only the EHG fast-wave, low-frequency band (FWL), then use a bivariate piecewise stationary pre-segmentation (bPSP) algorithm to segment the EHG signal into stationary parts. The second pillar addresses the estimation of connectivity and directionality using three methods: nonlinear correlation coefficient (h), general synchronization (H), and Granger causality (GC). The third pillar is related to signal classification and discrimination between pregnancy and labor using receiver operating curves (ROC) and connectivity and direction maps. For this purpose, we analyze the impact of four factors on data processing efficiency: i) method of connectivity detection, ii) effect of piecewise stationary segmentation preprocessing, iii) retained frequency content and iv) electrode configuration used for EHG recording (bipolar vs. unipolar).
Our results show that piecewise signal segmentation and filtering considerably improves classification performance and statistical significance for some connectivity methods, in particular the h. To this end we propose a new approach (detailed below) for h called Filtered-Windowed (FW) h that better highlights the differences between pregnancy and labor in the connectivity matrix and directionality maps.
This is the first comparative study of the effects of multiple processing factors on connectivity measurement efficiency. Our results indicate that appropriate preprocessing can improve the differentiation of pregnancy and labor-induced contraction signals and may lead to innovative applications in the prevention of preterm labor.
近年来，人们对电子宫图（EHG）信号越来越感兴趣，认为它是评估子宫收缩同步性的一种手段。有几项研究指出，信号处理的质量——因此也是测量结果的解释——受到测量技术的选择以及 EHG 信号中存在非平稳频率成分的显著影响。据我们所知，EHG 信号处理质量受时间变化的影响尚未得到充分研究。如何最好地处理 EHG 信号，以区分分娩引起的收缩和无害的产前收缩，仍然是一个悬而未决的问题。
我们的方法学基于三个支柱。第一个支柱是 EHG 预处理的新方法，我们应用二阶巴特沃斯滤波器来保留 EHG 快波、低频带（FWL），然后使用双变量分段平稳预分割（bPSP）算法将 EHG 信号分割为平稳部分。第二个支柱涉及使用三种方法估计连通性和方向性：非线性相关系数（h）、广义同步（H）和格兰杰因果关系（GC）。第三个支柱与使用接收者操作特征曲线（ROC）和连通性和方向性图在妊娠和分娩之间进行信号分类和区分有关。为此，我们分析了四个因素对数据处理效率的影响：i）连通性检测方法，ii）分段平稳性预处理的效果，iii）保留的频率内容，iv）用于 EHG 记录的电极配置（双极与单极）。
我们的结果表明，分段信号分割和滤波显著提高了某些连通性方法的分类性能和统计意义，特别是 h。为此，我们提出了一种新的 h 方法（下文详细介绍），称为滤波窗口（FW）h，它可以更好地突出连通性矩阵和方向性图中妊娠和分娩之间的差异。
这是首次对多种处理因素对连通性测量效率影响的比较研究。我们的结果表明，适当的预处理可以改善妊娠和分娩引起的收缩信号的区分，并且可能为预防早产带来创新应用。
