Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Camino de Vera s/n Ed.8B, 46022 Valencia, Spain; Servicio de Obstetricia y Ginecología, Área de la Salud de la Mujer, Hospital Universitario y Politécnico La Fe de Valencia, Bulevar Sur SN, 46033, Valencia, Spain.
Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Camino de Vera s/n Ed.8B, 46022 Valencia, Spain.
Comput Methods Programs Biomed. 2017 Jun;144:127-133. doi: 10.1016/j.cmpb.2017.03.018. Epub 2017 Mar 28.
Induction of labor (IOL) is a medical procedure used to initiate uterine contractions to achieve delivery. IOL entails medical risks and has a significant impact on both the mother's and newborn's well-being. The assistance provided by an automatic system to help distinguish patients that will achieve labor spontaneously from those that will need late-term IOL would help clinicians and mothers to take an informed decision about prolonging pregnancy. With this aim, we developed and evaluated predictive models using not only traditional obstetrical data but also electrophysiological parameters derived from the electrohysterogram (EHG).
EHG recordings were made on singleton term pregnancies. A set of 10 temporal and spectral parameters was calculated to characterize EHG bursts and a further set of 6 common obstetrical parameters was also considered in the predictive models design. Different models were implemented based on single layer Support Vector Machines (SVM) and with aggregation of majority voting of SVM (double layer), to distinguish between the two groups: term spontaneous labor (≤41 weeks of gestation) and IOL late-term labor. The areas under the curve (AUC) of the models were compared.
The obstetrical and EHG parameters of the two groups did not show statistically significant differences. The best results of non-contextualized single input parameter SVM models were achieved by the Bishop Score (AUC= 0.65) and GA at recording time (AUC= 0.68) obstetrical parameters. The EHG parameter median frequency, when contextualized with the two obstetrical parameters improved these results, reaching AUC= 0.76. Multiple input SVM obtained AUC= 0.70 for all EHG parameters. Aggregation of majority voting of SVM models using contextualized EHG parameters achieved the best result AUC= 0.93.
Measuring the electrophysiological uterine condition by means of electrohysterographic recordings yielded a promising clinical decision support system for distinguishing patients that will spontaneously achieve active labor before the end of full term from those who will require late term IOL. The importance of considering these EHG measurements in the patient's individual context was also shown by combining EHG parameters with obstetrical parameters. Clinicians considering elective labor induction would benefit from this technique.
引产（IOL）是一种用于引发子宫收缩以实现分娩的医疗程序。IOL 涉及医疗风险，对母婴的健康都有重大影响。一个自动系统提供的帮助，可以帮助区分那些将自然分娩的患者和那些需要晚期 IOL 的患者，这将有助于临床医生和母亲在延长妊娠方面做出明智的决策。为此，我们不仅使用传统的产科数据，还使用源自电子宫描记图（EHG）的电生理参数来开发和评估预测模型。
对单胎足月妊娠进行 EHG 记录。计算了一组 10 个时频参数，以描述 EHG 爆发，还考虑了另一组 6 个常见产科参数，用于预测模型设计。基于单层支持向量机（SVM）和 SVM 多数投票的聚合（双层），实施了不同的模型，以区分两组：足月自发性分娩（<41 孕周）和 IOL 晚期分娩。比较了模型的曲线下面积（AUC）。
两组的产科和 EHG 参数没有显示出统计学上的显著差异。非上下文化单输入参数 SVM 模型的最佳结果是由 Bischof 评分（AUC=0.65）和记录时的 GA（AUC=0.68）产科参数获得的。EHG 参数中位数频率，当与两个产科参数结合时，可以改善这些结果，达到 AUC=0.76。多输入 SVM 获得了所有 EHG 参数的 AUC=0.70。使用上下文化 EHG 参数的 SVM 模型的多数投票聚合获得了最佳结果 AUC=0.93。
通过电子宫描记图记录测量子宫的电生理状况，为区分那些将在足月前自然分娩的患者和那些需要晚期 IOL 的患者提供了一种有前途的临床决策支持系统。还通过将 EHG 参数与产科参数相结合，显示了考虑这些 EHG 测量在患者个体背景下的重要性。考虑选择性引产的临床医生将受益于这项技术。
