PDF(Pubmed)
Abstract：
Cardiotocography (CTG) is widely used to assess fetal well-being. CTG is typically obtained using ultrasound and autocorrelation methods, which extract periodicity from the signal to calculate the heart rate. However, during labor, maternal vessel pulsations can be measured, resulting in the output of the maternal heart rate (MHR). Since the autocorrelation output is displayed as fetal heart rate (FHR), there is a risk that obstetricians may mistakenly evaluate the fetal condition based on MHR, potentially overlooking the necessity for medical intervention. This study proposes a method that utilizes Doppler ultrasound (DUS) signals and artificial intelligence (AI) to determine whether the heart rate obtained by autocorrelation is of fetal origin. We developed a system to simultaneously record DUS signals and CTG and obtained data from 425 cases. The midwife annotated the DUS signals by auditory differentiation, providing data for AI, which included 30,160 data points from the fetal heart and 2160 data points from the maternal vessel. Comparing the classification accuracy of the AI model and a simple mathematical method, the AI model achieved the best performance, with an area under the curve (AUC) of 0.98. Integrating this system into fetal monitoring could provide a new indicator for evaluating CTG quality.
摘要：
心脏描记术（CTG）广泛用于评估胎儿的健康状况。CTG通常使用超声和自相关方法获得，从信号中提取周期性以计算心率。然而,在劳动期间,可以测量母体血管搏动，导致母体心率（MHR）的输出。由于自相关输出显示为胎儿心率（FHR），产科医生可能会错误地根据MHR评估胎儿状况，可能忽视了医疗干预的必要性。这项研究提出了一种利用多普勒超声（DUS）信号和人工智能（AI）来确定通过自相关获得的心率是否是胎儿起源的方法。我们开发了一个同时记录DUS信号和CTG的系统，并从425例病例中获得了数据。助产士通过听觉微分注释了DUS信号，为AI提供数据，其中包括来自胎儿心脏的30,160个数据点和来自母体血管的2160个数据点。比较AI模型的分类精度和简单的数学方法，AI模型实现了最佳性能，曲线下面积（AUC）为0.98。将该系统集成到胎儿监护中可以为评估CTG质量提供新的指标。
