Electronic fetal heart rate (FHR) monitoring is a technical means to evaluate the state of the fetus in the uterus by monitoring FHR. The main purpose is to detect intrauterine hypoxia and take corresponding medical measures timely. Because the fetus sleeps quietly for up to 1 hour sometimes, ultrasound Doppler is not easy to continuously detect for a long time. The electronic fetal monitor obtains the fetal heart rate, which not only improves the accuracy and comfort, but also the convenient implementation of long-term monitoring. It is beneficial to reduce perinatal fetal morbidity and mortality. This study used maternal–fetal Holter monitor which is based on the technology of fetal electrocardiograph (FECG) to collect the FHR, and then design algorithm to extract the baseline FHR, acceleration, variation, sleep-wake cycle and nonlinear parameters. There were significant differences in the 22 parameters between the normal and the suspicious group. Using the 22 characteristic parameters, the support vector machine was used to classify the normal and the suspected group of fetuses. 80% of the data was used to train a classification model. The remaining 20% of the data was used as a test set and its accuracy reached 93.75%.

中文翻译：

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基于非压力测试中胎儿心电图的胎儿类型分类

电子胎心率（FHR）监测是通过监测FHR来评估胎儿在子宫内状态的技术手段。主要目的是发现宫内缺氧并及时采取相应的医疗措施。由于胎儿的睡眠时间有时长达1小时，超声多普勒不易长时间连续检测。电子胎监获取胎心，不仅提高了准确度和舒适度，也方便了长期监测的实施。有利于降低围产期胎儿的发病率和死亡率。本研究使用基于胎儿心电图（FECG）技术的母胎动态心电图采集FHR，然后设计算法提取基线FHR、加速度、变异、睡眠-觉醒周期和非线性参数。正常组和可疑组的22个参数有显着差异。使用22个特征参数，使用支持向量机对正常和疑似胎儿组进行分类。80% 的数据用于训练分类模型。剩下的20%的数据作为测试集，准确率达到93.75%。