Cardiotocography (CTG) is a commonly used technique of electronic fetal monitoring (EFM) for evaluating fetal well-being, which has the disadvantage of lower diagnostic rate caused by subjective factors. To reduce the rate of misdiagnosis and assist obstetricians in making accurate medical decisions, this paper proposed an intelligent assessment approach for analyzing fetal state based on fetal heart rate (FHR) signals. First, the FHR signals from the public database of the Czech Technical University-University Hospital in Brno (CTU-UHB) was preprocessed, and the comprehensive features were extracted. Then the optimal feature subset based on the k-nearest neighbor (KNN) genetic algorithm (GA) was selected. At last the classification using least square support vector machine (LS-SVM) was executed. The experimental results showed that the classification of fetal state achieved better performance using the proposed method in this paper: the accuracy is 91%, sensitivity is 89%, specificity is 94%, quality index is 92%, and area under the receiver operating characteristic curve is 92%, which can assist clinicians in assessing fetal state effectively.
胎心宫缩图是一种临床常用的评估胎儿健康状况的电子监护技术，具有易受主观因素影响导致诊断率较低的缺点。为降低误诊率，辅助医生做出准确的医疗决策，本文提出了一种基于胎心率信号分析胎儿状态的智能评估方法。首先，本文将来自捷克技术大学—布尔诺大学医院公开数据库的信号进行预处理后，对其中的胎心率信号进行多模态特征提取，然后利用设计的基于 k—最近邻遗传算法选择最优特征子集，最后采用最小二乘支持向量机法对其分类。实验结果显示，利用本文提出的方法对胎儿状态进行分类，其准确度可达 91%，灵敏度为 89%，特异度为 94%，质量指标为 92%，受试者工作特征曲线下面积为 92%，具有较好的分类性能，可辅助临床医生对胎儿状态做出有效评估。.
Keywords: cardiotocography; feature extraction; fetal heart rate; genetic algorithm; least square support vector machine.