Mas-Cabo Javier, Ye-Lin Yiyao, Garcia-Casado Javier, Díaz-Martinez Alba, Perales-Marin Alfredo, Monfort-Ortiz Rogelio, Roca-Prats Alba, López-Corral Ángel, Prats-Boluda Gema
Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain.
Servicio de Obstetricia, H.U. P. La Fe, 46026 Valencia, Spain.
Entropy (Basel). 2020 Jul 5;22(7):743. doi: 10.3390/e22070743.
Electrohysterography (EHG) has been shown to provide relevant information on uterine activity and could be used for predicting preterm labor and identifying other maternal fetal risks. The extraction of high-quality robust features is a key factor in achieving satisfactory prediction systems from EHG. Temporal, spectral, and non-linear EHG parameters have been computed to characterize EHG signals, sometimes obtaining controversial results, especially for non-linear parameters. The goal of this work was to assess the performance of EHG parameters in identifying those robust enough for uterine electrophysiological characterization. EHG signals were picked up in different obstetric scenarios: antepartum, including women who delivered on term, labor, and post-partum. The results revealed that the 10th and 90th percentiles, for parameters with falling and rising trends as labor approaches, respectively, differentiate between these obstetric scenarios better than median analysis window values. Root-mean-square amplitude, spectral decile 3, and spectral moment ratio showed consistent tendencies for the different obstetric scenarios as well as non-linear parameters: Lempel-Ziv, sample entropy, spectral entropy, and SD1/SD2 when computed in the fast wave high bandwidth. These findings would make it possible to extract high quality and robust EHG features to improve computer-aided assessment tools for pregnancy, labor, and postpartum progress and identify maternal fetal risks.
子宫电图（EHG）已被证明能提供有关子宫活动的相关信息，可用于预测早产并识别其他母婴风险。提取高质量的稳健特征是从EHG实现令人满意的预测系统的关键因素。已计算时间、频谱和非线性EHG参数来表征EHG信号，有时会得到有争议的结果，尤其是对于非线性参数。这项工作的目标是评估EHG参数在识别足以用于子宫电生理特征描述的参数方面的性能。在不同的产科场景中采集EHG信号：产前，包括足月分娩的女性、分娩期和产后。结果显示，对于随着分娩临近分别具有下降和上升趋势的参数，第10和第90百分位数比中位数分析窗口值能更好地区分这些产科场景。均方根振幅、频谱十分位数3和频谱矩比对于不同的产科场景以及非线性参数（在快波高带宽中计算时的Lempel-Ziv、样本熵、频谱熵和SD1/SD2）显示出一致的趋势。这些发现将有可能提取高质量和稳健的EHG特征，以改进用于妊娠、分娩和产后进展的计算机辅助评估工具，并识别母婴风险。
