Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, China.
Obstetrical Department, Peking Union Medical College Hospital, Beijing, China.
Technol Health Care. 2022;30(S1):235-242. doi: 10.3233/THC-228022.
As an essential indicator of labour and delivery, uterine contraction (UC) can be detected by manual palpation, external tocodynamometry and internal uterine pressure catheter. However, these methods are not applicable for long-term monitoring.
This paper aims to recognize UCs with electrohysterogram (EHG) and find the best electrode combination with fewer electrodes.
112 EHG recordings were collected by our bespoke device in our study. Thirteen features were extracted from EHG segments of UC and non-UC. Four classifiers of the decision tree, support vector machine (SVM), artificial neural network, and convolutional neural network were established to identify UCs. The optimal classifier among them was determined by comparing their classification results. The optimal classifier was applied to evaluate all the possible electrode combinations with one to eight electrodes.
The results showed that SVM achieved the best classification capability. With SVM, the combination of electrodes on the right part of the uterine fundus and around the uterine body's median axis achieved the overall best performance.
The optimal electrode combination with fewer electrodes was confirmed to improve the clinical application for long-term monitoring of UCs.
子宫收缩（UC）作为分娩的重要指标，可以通过手动触诊、外部胎儿监护和内部子宫压力导管来检测。然而，这些方法不适用于长期监测。
本文旨在用电极肌电图（EHG）识别 UC，并找到具有较少电极的最佳电极组合。
本研究使用我们的定制设备收集了 112 份 EHG 记录。从 UC 和非 UC 的 EHG 段中提取了 13 个特征。建立了决策树、支持向量机（SVM）、人工神经网络和卷积神经网络这四种分类器来识别 UC。通过比较它们的分类结果，确定了其中最优的分类器。然后，将最优分类器应用于评估具有 1 到 8 个电极的所有可能电极组合。
结果表明，SVM 具有最佳的分类能力。使用 SVM，位于子宫底右侧和子宫体中部轴线周围的电极组合总体表现最佳。
证实了具有较少电极的最佳电极组合可以提高 UC 长期监测的临床应用。
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