Automatic Classification of Fetal Heart Rate Based on Convolutional Neural Network

Fetal heart rate (FHR) is very significant to evaluate the status of fetus. However, based on traditional classification criteria is not accurate. With the rapid development of computer information technology, computer technology is vital for the analysis of FHR in electronic fetal monitoring (EFM). FHR is divided into three classes as: 1) normal; 2) suspicious; and 3) abnormal. Through the cooperation with the hospital, we got 4473 records, including 3012 normal, 1024 suspicious, 437 abnormal records by our EFM system. In order to improve the accuracy of fetal status assessment, high 1-D FHR records are divided into ten d-window segments, and then use convolutional neural network (CNN) to process the data in parallel. Finally, we use the voting method to determine the class of FHR records. We also made a comparative experiment, the feature extraction method based on basic statistics is used to extract the features of FHR. And then the features were applied as the input to support vector machine (SVM) and multilayer perceptron (MLP) to classify. According to the results of the experiment, the accuracy of classification of SVM, MLP, and CNN are 79.66%, 85.98%, and 93.24%, respectively.