Computer analysis of cardiotocography (CTG) is very significant to evaluate fetal status. However, current computer analysis based on traditional classification criteria is not ideal. In order to improve the accuracy of fetal status assessment, we proposed a new method. The new method improves the classification criteria and uses fuzzy set to represent the CTG parameters. And then feature vector is formed by that set to represent the CTG signal. By calculating and comparing the Euclidean distance between the signal feature vector and the standard state feature vector, the corresponding fetal status of the signal can be determined. Experiments showed that compared to the results of the first expert, the accuracy rate of new method was 88.3% which was higher than that (69.9%) of the traditional method, and the false positive rate of new method was 7.2% which was much lower than that (34.9%) of traditional methods. While compared to the results of the second expert, the accuracy of new method was 90.3% which was higher than that (66.0%) of the traditional method, and the false positive rate of new method was 9.0% which was well below the 38.2% of the traditional method. Thus the new method is reliable and effective.
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.