Electronic fetal monitoring is used to evaluate fetal well-being by assessing fetal heart activity. The signals produced by the fetal heart carry valuable information about fetal health, but due to non-stationarity and present interference, their processing, analysis and interpretation is considered to be very challenging. Therefore, medical technologies equipped with Artificial Intelligence algorithms are rapidly evolving into clinical practice and provide solutions in the key application areas: noise suppression, feature detection and fetal state classification. The use of artificial intelligence and machine learning in the field of electronic fetal monitoring has demonstrated the efficiency and superiority of such techniques compared to conventional algorithms, especially due to their ability to predict, learn and efficiently handle dynamic Big data. Combining multiple algorithms and optimizing them for given purpose enables timely and accurate diagnosis of fetal health state. This review summarizes the currently used algorithms based on artificial intelligence and machine learning in the field of electronic fetal monitoring, outlines its advantages and limitations, as well as future challenges which remain to be solved.

电子胎儿监护用于通过评估胎儿心脏活动来评估胎儿的健康状况。胎儿心脏产生的信号携带着有关胎儿健康的宝贵信息，但由于非平稳性和存在干扰，其处理、分析和解释被认为非常具有挑战性。因此，配备人工智能算法的医疗技术正在迅速发展到临床实践，并在噪声抑制、特征检测和胎儿状态分类等关键应用领域提供解决方案。人工智能和机器学习在电子胎儿监护领域的使用已经证明了此类技术相对于传统算法的效率和优越性，特别是由于它们具有预测、学习和有效处理动态大数据的能力。结合多种算法并针对特定目的对其进行优化，可以及时、准确地诊断胎儿的健康状态。本文总结了目前在电子胎儿监护领域使用的基于人工智能和机器学习的算法，概述了其优点和局限性，以及未来有待解决的挑战。