Faculty of Mechanical Engineering, Bialystok University of Technology, Wiejska 45C, 15-351 Białystok, Poland.
Faculty of Mechanical Engineering, Bialystok University of Technology, Wiejska 45C, 15-351 Białystok, Poland.
Comput Methods Programs Biomed. 2018 Jan;153:227-236. doi: 10.1016/j.cmpb.2017.10.018. Epub 2017 Oct 17.
Common methods for data analysis are mainly based on linear concepts, but in recent years nonlinear dynamics methods have been introduced. It is a well-known fact that In typical biological systems lack of stationarity and rather sudden changes of state are the properties distinguishing them from each other. There is an urgent need to better understand the mechanical activity of the myometrium (its contractility) to find a solution for preterm delivery problem, the largest cause of neonatal deaths and morbidity. The electrohysterographic signal (EHG) is a good non-linear, bioelectrical indicator for the detection and identification of term and preterm birth.
The material of the study consists of EHG signals, obtained from 20 patients between the 24th and the 28th week of pregnancy with threatened preterm labor. The women were divided into two groups: those delivering after more than 7 days - group A (n = 10) and women delivering within 7 days - group B (n = 10). In this paper, an analysis of bioelectrical signals was performed by recurrence quantification analysis (RQA) and principal component analysis (PCA) to distinguish particular patterns for term and preterm birth. To date, these methods have not been used for the evaluation of bioelectrical activity in the uterus. To train novel classifiers for the EHG signals Support Vectors Machine classifications (multiclass SVM) was used. Statistical analysis was performed by means of non-parametric Mann-Whitney test.
From among eleven parameters obtained from recurrence quantification analysis, five most appropriate were chosen: Recurrence Rate, Determinism, Laminarity, Entropy and Recurrence Period Density Entropy. Significant increase (p < .001) of Recurrence Rate was found in patients from group B, while increase of parameters, besides Laminarity, was found in patients from group A. The accuracy of classification obtained as a result of the analysis increased to 83,32%.
We showed that the respectively selected recurrence quantificators obtained for that time series could be used to classify all those signals to the appropriate group. The proposed analysis could help in detecting preterm labor based on the EHG signal dynamics.
常用数据分析方法主要基于线性概念，但近年来已引入非线性动力学方法。众所周知，在典型的生物系统中，缺乏平稳性，状态的突然变化是它们相互区别的特征。迫切需要更好地了解子宫肌层的机械活动（其收缩性），以找到解决早产问题的方法，早产是新生儿死亡和发病的最大原因。电子宫图信号（EHG）是检测和识别足月和早产的良好非线性生物电指标。
本研究的材料包括 20 名处于 24 至 28 孕周、有早产威胁的孕妇的 EHG 信号。这些女性被分为两组：超过 7 天分娩的组 A（n=10）和 7 天内分娩的组 B（n=10）。在本文中，通过递归定量分析（RQA）和主成分分析（PCA）对生物电信号进行分析，以区分足月和早产的特定模式。迄今为止，这些方法尚未用于评估子宫的生物电活动。为了训练用于 EHG 信号的新型分类器，使用了支持向量机分类（多类 SVM）。通过非参数 Mann-Whitney 检验进行统计分析。
从递归定量分析获得的十一个参数中，选择了五个最合适的参数：递归率、确定性、层流性、熵和递归周期密度熵。从组 B 的患者中发现递归率显著增加（p<0.001），而从组 A 的患者中发现除层流性外，其他参数也增加。分析得到的分类准确率提高到 83.32%。
我们表明，为该时间序列分别选择的递归定量分析器可用于将所有这些信号分类到适当的组中。所提出的分析可以帮助基于 EHG 信号动力学检测早产。
